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null | https://openreview.net/forum?id=2INcTKPBy4 | @inproceedings{
livni2024the,
title={The Sample Complexity of Gradient Descent in Stochastic Convex Optimization},
author={Roi Livni},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=2INcTKPBy4}
} | We analyze the sample complexity of full-batch Gradient Descent (GD) in the setup of non-smooth Stochastic Convex Optimization. We show that the generalization error of GD, with common choice of hyper-parameters, can be $\tilde \Theta(d/m+1/\sqrt{m})$, where d is the dimension and m is the sample size. This matches the sample complexity of \emph{worst-case} empirical risk minimizers. That means that, in contrast with other algorithms, GD has no advantage over naive ERMs. Our bound follows from a new generalization bound that depends on both the dimension as well as the learning rate and number of iterations. Our bound also shows that, for general hyper-parameters, when the dimension is strictly larger than number of samples, $T=\Omega(1/\epsilon^4)$ iterations are necessary to avoid overfitting. This resolves an open problem by Schlisserman et al.23 and Amir er Al.21, and improves over previous lower bounds that demonstrated that the sample size must be at least square root of the dimension. | The Sample Complexity of Gradient Descent in Stochastic Convex Optimization | [
"Roi Livni"
] | NeurIPS.cc/2024/Conference | 2404.04931 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=2HvgvB4aWq | @inproceedings{
seminara2024differentiable,
title={Differentiable Task Graph Learning: Procedural Activity Representation and Online Mistake Detection from Egocentric Videos},
author={Luigi Seminara and Giovanni Maria Farinella and Antonino Furnari},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=2HvgvB4aWq}
} | Procedural activities are sequences of key-steps aimed at achieving specific goals. They are crucial to build intelligent agents able to assist users effectively. In this context, task graphs have emerged as a human-understandable representation of procedural activities, encoding a partial ordering over the key-steps. While previous works generally relied on hand-crafted procedures to extract task graphs from videos, in this paper, we propose an approach based on direct maximum likelihood optimization of edges' weights, which allows gradient-based learning of task graphs and can be naturally plugged into neural network architectures. Experiments on the CaptainCook4D dataset demonstrate the ability of our approach to predict accurate task graphs from the observation of action sequences, with an improvement of +16.7% over previous approaches. Owing to the differentiability of the proposed framework, we also introduce a feature-based approach, aiming to predict task graphs from key-step textual or video embeddings, for which we observe emerging video understanding abilities. Task graphs learned with our approach are also shown to significantly enhance online mistake detection in procedural egocentric videos, achieving notable gains of +19.8% and +7.5% on the Assembly101-O and EPIC-Tent-O datasets. Code for replicating the experiments is available at https://github.com/fpv-iplab/Differentiable-Task-Graph-Learning. | Differentiable Task Graph Learning: Procedural Activity Representation and Online Mistake Detection from Egocentric Videos | [
"Luigi Seminara",
"Giovanni Maria Farinella",
"Antonino Furnari"
] | NeurIPS.cc/2024/Conference | 2406.01486 | [
"https://github.com/fpv-iplab/differentiable-task-graph-learning"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=2GQeCbhxVy | @inproceedings{
leong2024the,
title={The Star Geometry of Critic-Based Regularizer Learning},
author={Oscar Leong and Eliza O'Reilly and Yong Sheng Soh},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=2GQeCbhxVy}
} | Variational regularization is a classical technique to solve statistical inference tasks and inverse problems, with modern data-driven approaches parameterizing regularizers via deep neural networks showcasing impressive empirical performance. Recent works along these lines learn task-dependent regularizers. This is done by integrating information about the measurements and ground-truth data in an unsupervised, critic-based loss function, where the regularizer attributes low values to likely data and high values to unlikely data. However, there is little theory about the structure of regularizers learned via this process and how it relates to the two data distributions. To make progress on this challenge, we initiate a study of optimizing critic-based loss functions to learn regularizers over a particular family of regularizers: gauges (or Minkowski functionals) of star-shaped bodies. This family contains regularizers that are commonly employed in practice and shares properties with regularizers parameterized by deep neural networks. We specifically investigate critic-based losses derived from variational representations of statistical distances between probability measures. By leveraging tools from star geometry and dual Brunn-Minkowski theory, we illustrate how these losses can be interpreted as dual mixed volumes that depend on the data distribution. This allows us to derive exact expressions for the optimal regularizer in certain cases. Finally, we identify which neural network architectures give rise to such star body gauges and when do such regularizers have favorable properties for optimization. More broadly, this work highlights how the tools of star geometry can aid in understanding the geometry of unsupervised regularizer learning. | The Star Geometry of Critic-Based Regularizer Learning | [
"Oscar Leong",
"Eliza O'Reilly",
"Yong Sheng Soh"
] | NeurIPS.cc/2024/Conference | 2408.16852 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=2AIwiIkE0s | @inproceedings{
ho2024vision,
title={Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights},
author={Sy-Tuyen Ho and Tuan Van Vo and Somayeh Ebrahimkhani and Ngai-man Cheung},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=2AIwiIkE0s}
} | While Vision Transformer (ViT) have achieved success across various machine learning tasks, deploying them in real-world scenarios faces a critical challenge: generalizing under Out-of-Distribution (OoD) shifts. A crucial research gap remains in understanding how to design ViT architectures – both manually and automatically – to excel in OoD generalization. **To address this gap,** we introduce OoD-ViT-NAS, the first systematic benchmark for ViT Neural Architecture Search (NAS) focused on OoD generalization. This comprehensive benchmark includes 3,000 ViT architectures of varying model computational budgets evaluated on common large-scale OoD datasets. With this comprehensive benchmark at hand, we analyze the factors that contribute to the OoD generalization of ViT architecture. Our analysis uncovers several key insights. Firstly, we show that ViT architecture designs have a considerable impact on OoD generalization. Secondly, we observe that In-Distribution (ID) accuracy might not be a very good indicator of OoD accuracy. This underscores the risk that ViT architectures optimized for ID accuracy might not perform well under OoD shifts. Thirdly, we conduct the first study to explore NAS for ViT’s OoD robustness. Specifically, we study 9 Training-free NAS for their OoD generalization performance on our benchmark. We observe that existing Training-free NAS are largely ineffective in predicting OoD accuracy despite their effectiveness at predicting ID accuracy. Moreover, simple proxies like #Param or #Flop surprisingly outperform more complex Training-free NAS in predicting ViTs OoD accuracy. Finally, we study how ViT architectural attributes impact OoD generalization. We discover that increasing embedding dimensions of a ViT architecture generally can improve the OoD generalization. We show that ViT architectures in our benchmark exhibit a wide range of OoD accuracy, with up to 11.85% for some OoD shift, prompting the importance to study ViT architecture design for OoD. We firmly believe that our OoD-ViT-NAS benchmark and our analysis can catalyze and streamline important research on understanding how ViT architecture designs influence OoD generalization. **Our OoD-NAS-ViT benchmark and code are available at [https://hosytuyen.github.io/projects/OoD-ViT-NAS](https://hosytuyen.github.io/projects/OoD-ViT-NAS)** | Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights | [
"Sy-Tuyen Ho",
"Tuan Van Vo",
"Somayeh Ebrahimkhani",
"Ngai-man Cheung"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=28bFUt6rUY | @inproceedings{
zhao2024evolvedirector,
title={EvolveDirector: Approaching Advanced Text-to-Image Generation with Large Vision-Language Models},
author={Rui Zhao and Hangjie Yuan and Yujie Wei and Shiwei Zhang and Yuchao Gu and Lingmin Ran and Xiang Wang and Jay Zhangjie Wu and David Junhao Zhang and Yingya Zhang and Mike Zheng Shou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=28bFUt6rUY}
} | Recent advancements in generation models have showcased remarkable capabilities in generating fantastic content. However, most of them are trained on proprietary high-quality data, and some models withhold their parameters and only provide accessible application programming interfaces (APIs), limiting their benefits for downstream tasks. To explore the feasibility of training a text-to-image generation model comparable to advanced models using publicly available resources, we introduce EvolveDirector. This framework interacts with advanced models through their public APIs to obtain text-image data pairs to train a base model. Our experiments with extensive data indicate that the model trained on generated data of the advanced model can approximate its generation capability. However, it requires large-scale samples of 10 million or more. This incurs significant expenses in time, computational resources, and especially the costs associated with calling fee-based APIs. To address this problem, we leverage pre-trained large vision-language models (VLMs) to guide the evolution of the base model. VLM continuously evaluates the base model during training and dynamically updates and refines the training dataset by the discrimination, expansion, deletion, and mutation operations. Experimental results show that this paradigm significantly reduces the required data volume. Furthermore, when approaching multiple advanced models, EvolveDirector can select the best samples generated by them to learn powerful and balanced abilities. The final trained model Edgen is demonstrated to outperform these advanced models. The framework EvolveDiretor and the trained model Edgen will be fully open-sourced to benefit the downstream tasks. | EvolveDirector: Approaching Advanced Text-to-Image Generation with Large Vision-Language Models | [
"Rui Zhao",
"Hangjie Yuan",
"Yujie Wei",
"Shiwei Zhang",
"Yuchao Gu",
"Lingmin Ran",
"Xiang Wang",
"Jay Zhangjie Wu",
"David Junhao Zhang",
"Yingya Zhang",
"Mike Zheng Shou"
] | NeurIPS.cc/2024/Conference | 2410.07133 | [
"https://github.com/showlab/evolvedirector"
] | https://huggingface.co/papers/2410.07133 | 6 | 18 | 2 | 11 | [
"ruizhaocv/Edgen"
] | [] | [] | [
"ruizhaocv/Edgen"
] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=26BdXIY3ik | @inproceedings{
dan2024tfgda,
title={{TFGDA}: Exploring Topology and Feature Alignment in Semi-supervised Graph Domain Adaptation through Robust Clustering},
author={Jun Dan and Weiming Liu and Chunfeng Xie and Hua Yu and Shunjie Dong and Yanchao Tan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=26BdXIY3ik}
} | Semi-supervised graph domain adaptation, as a branch of graph transfer learning, aims to annotate unlabeled target graph nodes by utilizing transferable knowledge learned from a label-scarce source graph. However, most existing studies primarily concentrate on aligning feature distributions directly to extract domain-invariant features, while ignoring the utilization of the intrinsic structure information in graphs. Inspired by the significance of data structure information in enhancing models' generalization performance, this paper aims to investigate how to leverage the structure information to assist graph transfer learning. To this end, we propose an innovative framework called TFGDA. Specially, TFGDA employs a structure alignment strategy named STSA to encode graphs' topological structure information into the latent space, greatly facilitating the learning of transferable features. To achieve a stable alignment of feature distributions, we also introduce a SDA strategy to mitigate domain discrepancy on the sphere. Moreover, to address the overfitting issue caused by label scarcity, a simple but effective RNC strategy is devised to guide the discriminative clustering of unlabeled nodes. Experiments on various benchmarks demonstrate the superiority of TFGDA over SOTA methods. | TFGDA: Exploring Topology and Feature Alignment in Semi-supervised Graph Domain Adaptation through Robust Clustering | [
"Jun Dan",
"Weiming Liu",
"Chunfeng Xie",
"Hua Yu",
"Shunjie Dong",
"Yanchao Tan"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=266nH7kLSV | @inproceedings{
tieu2024temporal,
title={Temporal Graph Neural Tangent Kernel with Graphon-Guaranteed},
author={Katherine Tieu and Dongqi Fu and Yada Zhu and Hendrik Hamann and Jingrui He},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=266nH7kLSV}
} | _Graph Neural Tangent Kernel_ (GNTK) fuses graph neural networks and graph kernels, simplifies the process of graph representation learning, interprets the training dynamics of graph neural networks, and serves various applications like protein identification, image segmentation, and social network analysis. In practice, graph data carries complex information among entities that inevitably evolves over time, and previous static graph neural tangent kernel methods may be stuck in the sub-optimal solution in terms of both effectiveness and efficiency. As a result, extending the advantage of GNTK to temporal graphs becomes a critical problem. To this end, we propose the temporal graph neural tangent kernel, which not only extends the simplicity and interpretation ability of GNTK to the temporal setting but also leads to rigorous temporal graph classification error bounds. Furthermore, we prove that when the input temporal graph grows over time in the number of nodes, our temporal graph neural tangent kernel will converge in the limit to the _graphon_ NTK value, which implies the transferability and robustness of the proposed kernel method, named **Temp**oral **G**raph **N**eural **T**angent **K**ernel with **G**raphon-**G**uaranteed or **Temp-G$^3$NTK**. In addition to the theoretical analysis, we also perform extensive experiments, not only demonstrating the superiority of Temp-G$^3$NTK in the temporal graph classification task, but also showing that Temp-G^3NTK can achieve very competitive performance in node-level tasks like node classification compared with various SOTA graph kernel and representation learning baselines. Our code is available at https://github.com/kthrn22/TempGNTK. | Temporal Graph Neural Tangent Kernel with Graphon-Guaranteed | [
"Katherine Tieu",
"Dongqi Fu",
"Yada Zhu",
"Hendrik Hamann",
"Jingrui He"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=25Ioxw576r | @inproceedings{
sun2024you,
title={You Only Cache Once: Decoder-Decoder Architectures for Language Models},
author={Yutao Sun and Li Dong and Yi Zhu and Shaohan Huang and Wenhui Wang and Shuming Ma and Quanlu Zhang and Jianyong Wang and Furu Wei},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=25Ioxw576r}
} | We introduce a decoder-decoder architecture, YOCO, for large language models, which only caches key-value pairs once. It consists of two components, i.e., a cross-decoder stacked upon a self-decoder. The self-decoder efficiently encodes global key-value (KV) caches that are reused by the cross-decoder via cross-attention. The overall model behaves like a decoder-only Transformer, although YOCO only caches once. The design substantially reduces GPU memory demands, yet retains global attention capability. Additionally, the computation flow enables prefilling to early exit without changing the final output, thereby significantly speeding up the prefill stage. Experimental results demonstrate that YOCO achieves favorable performance compared to Transformer in various settings of scaling up model size and number of training tokens. We also extend YOCO to 1M context length with near-perfect needle retrieval accuracy. The profiling results show that YOCO improves inference memory, prefill latency, and throughput by orders of magnitude across context lengths and model sizes. | You Only Cache Once: Decoder-Decoder Architectures for Language Models | [
"Yutao Sun",
"Li Dong",
"Yi Zhu",
"Shaohan Huang",
"Wenhui Wang",
"Shuming Ma",
"Quanlu Zhang",
"Jianyong Wang",
"Furu Wei"
] | NeurIPS.cc/2024/Conference | 2405.05254 | [
"https://github.com/microsoft/unilm/blob/master/YOCO/README.md"
] | https://huggingface.co/papers/2405.05254 | 1 | 9 | 1 | 9 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=232VcN8tSx | @inproceedings{
wang2024greats,
title={{GREATS}: Online Selection of High-Quality Data for {LLM} Training in Every Iteration},
author={Jiachen T. Wang and Tong Wu and Dawn Song and Prateek Mittal and Ruoxi Jia},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=232VcN8tSx}
} | Online batch selection methods offer an adaptive alternative to static training data selection by dynamically selecting data batches during training. However, existing methods either rely on impractical reference models or simple heuristics that may not capture true data informativeness. To address these limitations, we propose \emph{GREedy Approximation Taylor Selection} (GREATS), a principled and efficient online batch selection method that applies greedy algorithm to optimize the data batch quality approximated by Taylor expansion. We develop a series of techniques to scale GREATS to large-scale model training. Extensive experiments with large language models (LLMs) demonstrate that GREATS significantly improves training convergence speed and generalization performance. | GREATS: Online Selection of High-Quality Data for LLM Training in Every Iteration | [
"Jiachen T. Wang",
"Tong Wu",
"Dawn Song",
"Prateek Mittal",
"Ruoxi Jia"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=21tn63ee15 | @inproceedings{
jia2024g,
title={G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models},
author={Pengyue Jia and Yiding Liu and Xiaopeng Li and Xiangyu Zhao and Yuhao Wang and Yantong Du and Xiao Han and Xuetao Wei and Shuaiqiang Wang and Dawei Yin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=21tn63ee15}
} | Worldwide geolocalization aims to locate the precise location at the coordinate level of photos taken anywhere on the Earth. It is very challenging due to 1) the difficulty of capturing subtle location-aware visual semantics, and 2) the heterogeneous geographical distribution of image data. As a result, existing studies have clear limitations when scaled to a worldwide context. They may easily confuse distant images with similar visual contents, or cannot adapt to various locations worldwide with different amounts of relevant data. To resolve these limitations, we propose **G3**, a novel framework based on Retrieval-Augmented Generation (RAG). In particular, G3 consists of three steps, i.e., **G**eo-alignment, **G**eo-diversification, and **G**eo-verification to optimize both retrieval and generation phases of worldwide geolocalization. During Geo-alignment, our solution jointly learns expressive multi-modal representations for images, GPS and textual descriptions, which allows us to capture location-aware semantics for retrieving nearby images for a given query. During Geo-diversification, we leverage a prompt ensembling method that is robust to inconsistent retrieval performance for different image queries. Finally, we combine both retrieved and generated GPS candidates in Geo-verification for location prediction. Experiments on two well-established datasets IM2GPS3k and YFCC4k verify the superiority of G3 compared to other state-of-the-art methods. Our code is available online [https://github.com/Applied-Machine-Learning-Lab/G3](https://github.com/Applied-Machine-Learning-Lab/G3) for reproduction. | G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models | [
"Pengyue Jia",
"Yiding Liu",
"Xiaopeng Li",
"Xiangyu Zhao",
"Yuhao Wang",
"Yantong Du",
"Xiao Han",
"Xuetao Wei",
"Shuaiqiang Wang",
"Dawei Yin"
] | NeurIPS.cc/2024/Conference | 2405.14702 | [
"https://github.com/applied-machine-learning-lab/g3"
] | https://huggingface.co/papers/2405.14702 | 0 | 0 | 0 | 10 | [] | [
"Jia-py/MP16-Pro"
] | [] | [] | [
"Jia-py/MP16-Pro"
] | [] | 1 | poster |
null | https://openreview.net/forum?id=20QgErW5zH | @inproceedings{
wang2024drones,
title={Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond},
author={Zhechao Wang and Peirui Cheng and Minxing Chen and Pengju Tian and Zhirui Wang and Xinming Li and Xue Yang and Xian Sun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=20QgErW5zH}
} | Collaborative trajectory prediction can comprehensively forecast the future motion of objects through multi-view complementary information. However, it encounters two main challenges in multi-drone collaboration settings. The expansive aerial observations make it difficult to generate precise Bird's Eye View (BEV) representations. Besides, excessive interactions can not meet real-time prediction requirements within the constrained drone-based communication bandwidth. To address these problems, we propose a novel framework named "Drones Help Drones" (DHD). Firstly, we incorporate the ground priors provided by the drone's inclined observation to estimate the distance between objects and drones, leading to more precise BEV generation. Secondly, we design a selective mechanism based on the local feature discrepancy to prioritize the critical information contributing to prediction tasks during inter-drone interactions. Additionally, we create the first dataset for multi-drone collaborative prediction, named "Air-Co-Pred", and conduct quantitative and qualitative experiments to validate the effectiveness of our DHD framework. The results demonstrate that compared to state-of-the-art approaches, DHD reduces position deviation in BEV representations by over 20\% and requires only a quarter of the transmission ratio for interactions while achieving comparable prediction performance. Moreover, DHD also shows promising generalization to the collaborative 3D object detection in CoPerception-UAVs. | Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond | [
"Zhechao Wang",
"Peirui Cheng",
"Minxing Chen",
"Pengju Tian",
"Zhirui Wang",
"Xinming Li",
"Xue Yang",
"Xian Sun"
] | NeurIPS.cc/2024/Conference | 2405.14674 | [
"https://github.com/wangzcbruce/dhd"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=204YOrDHny | @inproceedings{
roy2024reparameterization,
title={Reparameterization invariance in approximate Bayesian inference},
author={Hrittik Roy and Marco Miani and Carl Henrik Ek and Philipp Hennig and Marvin Pf{\"o}rtner and Lukas Tatzel and S{\o}ren Hauberg},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=204YOrDHny}
} | Current approximate posteriors in Bayesian neural networks (BNNs) exhibit a crucial limitation: they fail to maintain invariance under reparameterization, i.e. BNNs assign different posterior densities to different parametrizations of identical functions. This creates a fundamental flaw in the application of Bayesian principles as it breaks the correspondence between uncertainty over the parameters with uncertainty over the parametrized function. In this paper, we investigate this issue in the context of the increasingly popular linearized Laplace approximation. Specifically, it has been observed that linearized predictives alleviate the common underfitting problems of the Laplace approximation. We develop a new geometric view of reparametrizations from which we explain the success of linearization. Moreover, we demonstrate that these reparameterization invariance properties can be extended to the original neural network predictive using a Riemannian diffusion process giving a straightforward algorithm for approximate posterior sampling, which empirically improves posterior fit. | Reparameterization invariance in approximate Bayesian inference | [
"Hrittik Roy",
"Marco Miani",
"Carl Henrik Ek",
"Philipp Hennig",
"Marvin Pförtner",
"Lukas Tatzel",
"Søren Hauberg"
] | NeurIPS.cc/2024/Conference | 2406.03334 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=1ziIqFo4Tj | @inproceedings{
kamhoua2024hope,
title={{HOPE}: Shape Matching Via Aligning Different K-hop Neighbourhoods},
author={Barakeel Fanseu Kamhoua and Huamin Qu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1ziIqFo4Tj}
} | Accurate and smooth shape matching is very hard to achieve. This is because for accuracy, one needs unique descriptors (signatures) on shapes that distinguish different vertices on a mesh accurately while at the same time being invariant to deformations. However, most existing unique shape descriptors are generally not smooth on the shape and are not noise-robust thus leading to non-smooth matches. On the other hand, for smoothness, one needs descriptors that are smooth and continuous on the shape. However, existing smooth descriptors are generally not unique and as such lose accuracy as they match neighborhoods (for smoothness) rather than exact vertices (for accuracy). In this work, we propose to use different k-hop neighborhoods of vertices as pairwise descriptors for shape matching. We use these descriptors in conjunction with local map distortion (LMD) to refine an initialized map for shape matching. We validate the effectiveness of our pipeline on benchmark datasets such as SCAPE, TOSCA, TOPKIDS, and others. | HOPE: Shape Matching Via Aligning Different K-hop Neighbourhoods | [
"Barakeel Fanseu Kamhoua",
"Huamin Qu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=1zVinhehks | @inproceedings{
wang2024graph,
title={Graph Classification via Reference Distribution Learning: Theory and Practice},
author={Zixiao Wang and Jicong Fan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1zVinhehks}
} | Graph classification is a challenging problem owing to the difficulty in quantifying the similarity between graphs or representing graphs as vectors, though there have been a few methods using graph kernels or graph neural networks (GNNs). Graph kernels often suffer from computational costs and manual feature engineering, while GNNs commonly utilize global pooling operations, risking the loss of structural or semantic information. This work introduces Graph Reference Distribution Learning (GRDL), an efficient and accurate graph classification method. GRDL treats each graph's latent node embeddings given by GNN layers as a discrete distribution, enabling direct classification without global pooling, based on maximum mean discrepancy to adaptively learned reference distributions. To fully understand this new model (the existing theories do not apply) and guide its configuration (e.g., network architecture, references' sizes, number, and regularization) for practical use, we derive generalization error bounds for GRDL and verify them numerically. More importantly, our theoretical and numerical results both show that GRDL has a stronger generalization ability than GNNs with global pooling operations. Experiments on moderate-scale and large-scale graph datasets show the superiority of GRDL over the state-of-the-art, emphasizing its remarkable efficiency, being at least 10 times faster than leading competitors in both training and inference stages. | Graph Classification via Reference Distribution Learning: Theory and Practice | [
"Zixiao Wang",
"Jicong Fan"
] | NeurIPS.cc/2024/Conference | 2408.11370 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1wxFznQWhp | @inproceedings{
lin2024delving,
title={Delving into the Reversal Curse: How Far Can Large Language Models Generalize?},
author={Zhengkai Lin and Zhihang Fu and Kai Liu and Liang Xie and Binbin Lin and Wenxiao Wang and Deng Cai and Yue Wu and Jieping Ye},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1wxFznQWhp}
} | While large language models (LLMs) showcase unprecedented capabilities, they also exhibit certain inherent limitations when facing seemingly trivial tasks.
A prime example is the recently debated "reversal curse", which surfaces when models, having been trained on the fact "A is B", struggle to generalize this knowledge to infer that "B is A".
In this paper, we examine the manifestation of the reversal curse across various tasks and delve into both the generalization abilities and the problem-solving mechanisms of LLMs. This investigation leads to a series of significant insights:
(1) LLMs are able to generalize to "B is A" when both A and B are presented in the context as in the case of a multiple-choice question.
(2) This generalization ability is highly correlated to the structure of the fact "A is B" in the training documents. For example, this generalization only applies to biographies structured in "[Name] is [Description]" but not to "[Description] is [Name]".
(3) We propose and verify the hypothesis that LLMs possess an inherent bias in fact recalling during knowledge application, which explains and underscores the importance of the document structure to successful learning.
(4) The negative impact of this bias on the downstream performance of LLMs can hardly be mitigated through training alone.
Based on these intriguing findings, our work not only presents a novel perspective for interpreting LLMs' generalization abilities from their intrinsic working mechanism but also provides new insights for the development of more effective learning methods for LLMs. | Delving into the Reversal Curse: How Far Can Large Language Models Generalize? | [
"Zhengkai Lin",
"Zhihang Fu",
"Kai Liu",
"Liang Xie",
"Binbin Lin",
"Wenxiao Wang",
"Deng Cai",
"Yue Wu",
"Jieping Ye"
] | NeurIPS.cc/2024/Conference | 2410.18808 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1we1V3MAHD | @inproceedings{
wu2024motionbooth,
title={MotionBooth: Motion-Aware Customized Text-to-Video Generation},
author={Jianzong Wu and Xiangtai Li and Yanhong Zeng and Jiangning Zhang and Qianyu Zhou and Yining Li and Yunhai Tong and Kai Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1we1V3MAHD}
} | In this work, we present MotionBooth, an innovative framework designed for animating customized subjects with precise control over both object and camera movements. By leveraging a few images of a specific object, we efficiently fine-tune a text-to-video model to capture the object's shape and attributes accurately. Our approach presents subject region loss and video preservation loss to enhance the subject's learning performance, along with a subject token cross-attention loss to integrate the customized subject with motion control signals. Additionally, we propose training-free techniques for managing subject and camera motions during inference. In particular, we utilize cross-attention map manipulation to govern subject motion and introduce a novel latent shift module for camera movement control as well. MotionBooth excels in preserving the appearance of subjects while simultaneously controlling the motions in generated videos. Extensive quantitative and qualitative evaluations demonstrate the superiority and effectiveness of our method. Models and codes will be made publicly available. | MotionBooth: Motion-Aware Customized Text-to-Video Generation | [
"Jianzong Wu",
"Xiangtai Li",
"Yanhong Zeng",
"Jiangning Zhang",
"Qianyu Zhou",
"Yining Li",
"Yunhai Tong",
"Kai Chen"
] | NeurIPS.cc/2024/Conference | 2406.17758 | [
""
] | https://huggingface.co/papers/2406.17758 | 4 | 18 | 1 | 8 | [
"jianzongwu/MotionBooth"
] | [
"jianzongwu/MotionBooth"
] | [] | [
"jianzongwu/MotionBooth"
] | [
"jianzongwu/MotionBooth"
] | [] | 1 | oral |
null | https://openreview.net/forum?id=1vPqOmqSfO | @inproceedings{
miani2024sketched,
title={Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information},
author={Marco Miani and Lorenzo Beretta and S{\o}ren Hauberg},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1vPqOmqSfO}
} | Current uncertainty quantification is memory and compute expensive, which hinders practical uptake. To counter, we develop Sketched Lanczos Uncertainty (SLU): an architecture-agnostic uncertainty score that can be applied to pre-trained neural networks with minimal overhead. Importantly, the memory use of SLU only grows logarithmically with the number of model parameters. We combine Lanczos' algorithm with dimensionality reduction techniques to compute a sketch of the leading eigenvectors of a matrix. Applying this novel algorithm to the Fisher information matrix yields a cheap and reliable uncertainty score. Empirically, SLU yields well-calibrated uncertainties, reliably detects out-of-distribution examples, and consistently outperforms existing methods in the low-memory regime. | Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information | [
"Marco Miani",
"Lorenzo Beretta",
"Søren Hauberg"
] | NeurIPS.cc/2024/Conference | 2409.15008 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1v4gKsyGfe | @inproceedings{
tomihari2024understanding,
title={Understanding Linear Probing then Fine-tuning Language Models from {NTK} Perspective},
author={Akiyoshi Tomihari and Issei Sato},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1v4gKsyGfe}
} | The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. This holds true for both in-distribution (ID) and out-of-distribution (OOD) data. One key reason for its success is the preservation of pre-trained features, achieved by obtaining a near-optimal linear head during LP. However, despite the widespread use of large language models, there has been limited exploration of more complex architectures such as Transformers. In this paper, we analyze the training dynamics of LP-FT for classification tasks on the basis of the neural tangent kernel (NTK) theory. Our analysis decomposes the NTK matrix into two components. This decomposition highlights the importance of the linear head norm alongside the prediction accuracy at the start of the FT stage. We also observe a significant increase in the linear head norm during LP, which stems from training with the cross-entropy (CE) loss. This increase in the linear head norm effectively reduces changes in learned features. Furthermore, we find that this increased norm can adversely affect model calibration, which can be corrected using temperature scaling. Additionally, we extend our analysis with the NTK to the low-rank adaptation (LoRA) method and validate its effectiveness. Our experiments using a Transformer-based model on multiple natural language processing datasets confirm our theoretical analysis. Our study demonstrates the effectiveness of LP-FT for fine-tuning language models. Code is available at https://github.com/tom4649/lp-ft_ntk. | Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective | [
"Akiyoshi Tomihari",
"Issei Sato"
] | NeurIPS.cc/2024/Conference | 2405.16747 | [
"https://github.com/tom4649/lp-ft_ntk"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1v0BPTR3AA | @inproceedings{
wang2024generalized,
title={Generalized Tensor Decomposition for Understanding Multi-Output Regression under Combinatorial Shifts},
author={Andong Wang and Yuning Qiu and Mingyuan Bai and Zhong Jin and Guoxu Zhou and Qibin Zhao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1v0BPTR3AA}
} | In multi-output regression, we identify a previously neglected challenge that arises from the inability of training distribution to cover all combinations of input features, leading to combinatorial distribution shift (CDS). To the best of our knowledge, this is the first work to formally define and address this problem. We tackle it through a novel tensor decomposition perspective, proposing the Functional t-Singular Value Decomposition (Ft-SVD) theorem which extends the classical tensor SVD to infinite and continuous feature domains, providing a natural tool for representing and analyzing multi-output functions. Within the Ft-SVD framework, we formulate the multi-output regression problem under CDS as a low-rank tensor estimation problem under the missing not at random (MNAR) setting, and introduce a series of assumptions about the true functions, training and testing distributions, and spectral properties of the ground-truth embeddings, making the problem more tractable.
To address the challenges posed by CDS in multi-output regression, we develop a tailored Double-Stage Empirical Risk Minimization (ERM-DS) algorithm that leverages the spectral properties of the embeddings and uses specific hypothesis classes in each frequency component to better capture the varying spectral decay patterns. We provide rigorous theoretical analyses that establish performance guarantees for the ERM-DS algorithm. This work lays a preliminary theoretical foundation for multi-output regression under CDS. | Generalized Tensor Decomposition for Understanding Multi-Output Regression under Combinatorial Shifts | [
"Andong Wang",
"Yuning Qiu",
"Mingyuan Bai",
"Zhong Jin",
"Guoxu Zhou",
"Qibin Zhao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=1u3qkG7BkQ | @inproceedings{
xia2024languagedriven,
title={Language-Driven Interactive Traffic Trajectory Generation},
author={Junkai XIA and Chenxin Xu and Qingyao Xu and Yanfeng Wang and Siheng Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1u3qkG7BkQ}
} | Realistic trajectory generation with natural language control is pivotal for advancing autonomous vehicle technology. However, previous methods focus on individual traffic participant trajectory generation, thus failing to account for the complexity of interactive traffic dynamics. In this work, we propose InteractTraj, the first language-driven traffic trajectory generator that can generate interactive traffic trajectories. InteractTraj interprets abstract trajectory descriptions into concrete formatted interaction-aware numerical codes and learns a mapping between these formatted codes and the final interactive trajectories. To interpret language descriptions, we propose a language-to-code encoder with a novel interaction-aware encoding strategy. To produce interactive traffic trajectories, we propose a code-to-trajectory decoder with interaction-aware feature aggregation that synergizes vehicle interactions with the environmental map and the vehicle moves. Extensive experiments show our method demonstrates superior performance over previous SoTA methods, offering a more realistic generation of interactive traffic trajectories with high controllability via diverse natural language commands. | Language-Driven Interactive Traffic Trajectory Generation | [
"Junkai XIA",
"Chenxin Xu",
"Qingyao Xu",
"Yanfeng Wang",
"Siheng Chen"
] | NeurIPS.cc/2024/Conference | 2405.15388 | [
"https://github.com/x1a-jk/interacttraj"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1sLdprsbmk | @inproceedings{
zhao2024can,
title={Can Models Learn Skill Composition from Examples?},
author={Haoyu Zhao and Simran Kaur and Dingli Yu and Anirudh Goyal and Sanjeev Arora},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1sLdprsbmk}
} | As large language models (LLMs) become increasingly advanced, their ability to exhibit compositional generalization---the capacity to combine learned skills in novel ways not encountered during training---has garnered significant attention. This type of generalization, particularly in scenarios beyond training data, is also of great interest in the study of AI safety and alignment. A recent study introduced the Skill-Mix evaluation, where models are tasked with composing a short paragraph demonstrating the use of a specified $k$-tuple of language skills. While small models struggled with composing even with $k=3$, larger models like GPT-4 performed reasonably well with $k=5$ and $6$.
In this paper, we employ a setup akin to Skill-Mix to evaluate the capacity of smaller models to learn compositional generalization from examples. Utilizing a diverse set of language skills---including rhetorical, literary, reasoning, theory of mind, and common sense---GPT was used to generate text samples that exhibit random subsets of $k$ skills. Subsequent fine-tuning of 7B and 13B parameter models on these combined skill texts, for increasing values of $k$, revealed the following findings: (1) Training on combinations of $k=2$ and $3$ skills results in noticeable improvements in the ability to compose texts with $k=4$ and $5$ skills, despite models never having seen such examples during training. (2) When skill categories are split into training and held-out groups, models significantly improve at composing texts with held-out skills during testing despite having only seen training skills during fine-tuning, illustrating the efficacy of the training approach even with previously unseen skills.
This study also suggests that incorporating skill-rich (potentially synthetic) text into training can substantially enhance the compositional capabilities of models. | Can Models Learn Skill Composition from Examples? | [
"Haoyu Zhao",
"Simran Kaur",
"Dingli Yu",
"Anirudh Goyal",
"Sanjeev Arora"
] | NeurIPS.cc/2024/Conference | 2409.19808 | [
""
] | https://huggingface.co/papers/2409.19808 | 1 | 8 | 2 | 5 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=1qfdCAXn6K | @inproceedings{
lv2024wasserstein,
title={Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation},
author={Jiaming Lv and Haoyuan Yang and Peihua Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1qfdCAXn6K}
} | Since pioneering work of Hinton et al., knowledge distillation based on Kullback-Leibler Divergence (KL-Div) has been predominant, and recently its variants have achieved compelling performance. However, KL-Div only compares probabilities of the corresponding category between the teacher and student while lacking a mechanism for cross-category comparison. Besides, KL-Div is problematic when applied to intermediate layers, as it cannot handle non-overlapping distributions and is unaware of geometry of the underlying manifold. To address these downsides, we propose a methodology of Wasserstein Distance (WD) based knowledge distillation. Specifically, we propose a logit distillation method called WKD-L based on discrete WD, which performs cross-category comparison of probabilities and thus can explicitly leverage rich interrelations among categories. Moreover, we introduce a feature distillation method called WKD-F, which uses a parametric method for modeling feature distributions and adopts continuous WD for transferring knowledge from intermediate layers. Comprehensive evaluations on image classification and object detection have shown (1) for logit distillation WKD-L outperforms very strong KL-Div variants; (2) for feature distillation WKD-F is superior to the KL-Div counterparts and state-of-the-art competitors. | Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation | [
"Jiaming Lv",
"Haoyuan Yang",
"Peihua Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=1ptdkwZbMG | @inproceedings{
bu2024closedloop,
title={Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation},
author={Qingwen Bu and Jia Zeng and Li Chen and Yanchao Yang and Guyue Zhou and Junchi Yan and Ping Luo and Heming Cui and Yi Ma and Hongyang Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1ptdkwZbMG}
} | Despite significant progress in robotics and embodied AI in recent years, deploying robots for long-horizon tasks remains a great challenge. Majority of prior arts adhere to an open-loop philosophy and lack real-time feedback, leading to error accumulation and undesirable robustness. A handful of approaches have endeavored to establish feedback mechanisms leveraging pixel-level differences or pre-trained visual representations, yet their efficacy and adaptability have been found to be constrained. Inspired by classic closed-loop control systems, we propose CLOVER, a closed-loop visuomotor control framework that incorporates feedback mechanisms to improve adaptive robotic control. CLOVER consists of a text-conditioned video diffusion model for generating visual plans as reference inputs, a measurable embedding space for accurate error quantification, and a feedback-driven controller that refines actions from feedback and initiates replans as needed. Our framework exhibits notable advancement in real-world robotic tasks and achieves state-of-the-art on CALVIN benchmark, improving by 8% over previous open-loop counterparts. Code and checkpoints are maintained at https://github.com/OpenDriveLab/CLOVER. | Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation | [
"Qingwen Bu",
"Jia Zeng",
"Li Chen",
"Yanchao Yang",
"Guyue Zhou",
"Junchi Yan",
"Ping Luo",
"Heming Cui",
"Yi Ma",
"Hongyang Li"
] | NeurIPS.cc/2024/Conference | 2409.09016 | [
"https://github.com/OpenDriveLab/CLOVER"
] | https://huggingface.co/papers/2409.09016 | 1 | 0 | 0 | 10 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=1po4j1Tv7O | @inproceedings{
mondal2024sampleefficient,
title={Sample-Efficient Constrained Reinforcement Learning with General Parameterization},
author={Washim Uddin Mondal and Vaneet Aggarwal},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1po4j1Tv7O}
} | We consider a constrained Markov Decision Problem (CMDP) where the goal of an agent is to maximize the expected discounted sum of rewards over an infinite horizon while ensuring that the expected discounted sum of costs exceeds a certain threshold. Building on the idea of momentum-based acceleration, we develop the Primal-Dual Accelerated Natural Policy Gradient (PD-ANPG) algorithm that ensures an $\epsilon$ global optimality gap and $\epsilon$ constraint violation with $\tilde{\mathcal{O}}((1-\gamma)^{-7}\epsilon^{-2})$ sample complexity for general parameterized policies where $\gamma$ denotes the discount factor. This improves the state-of-the-art sample complexity in general parameterized CMDPs by a factor of $\mathcal{O}((1-\gamma)^{-1}\epsilon^{-2})$ and achieves the theoretical lower bound in $\epsilon^{-1}$. | Sample-Efficient Constrained Reinforcement Learning with General Parameterization | [
"Washim Uddin Mondal",
"Vaneet Aggarwal"
] | NeurIPS.cc/2024/Conference | 2405.10624 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1ojAkTylz4 | @inproceedings{
lee2024ant,
title={{ANT}: Adaptive Noise Schedule for Time Series Diffusion Models},
author={Seunghan Lee and Kibok Lee and Taeyoung Park},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1ojAkTylz4}
} | Advances in diffusion models for generative artificial intelligence have recently propagated to the time series (TS) domain, demonstrating state-of-the-art performance on various tasks. However, prior works on TS diffusion models often borrow the framework of existing works proposed in other domains without considering the characteristics of TS data, leading to suboptimal performance. In this work, we
propose Adaptive Noise schedule for Time series diffusion models (ANT), which automatically predetermines proper noise schedules for given TS datasets based on their statistics representing non-stationarity. Our intuition is that an optimal noise schedule should satisfy the following desiderata: 1) It linearly reduces the non-stationarity of TS data so that all diffusion steps are equally meaningful, 2) the data is corrupted to the random noise at the final step, and 3) the number of steps is sufficiently large. The proposed method is practical for use in that it eliminates the necessity of finding the optimal noise schedule with a small additional cost to compute the statistics for given datasets, which can be done offline before training. We validate the effectiveness of our method across various tasks, including TS forecasting, refinement, and generation, on datasets from diverse domains. Code is available at this repository: https://github.com/seunghan96/ANT. | ANT: Adaptive Noise Schedule for Time Series Diffusion Models | [
"Seunghan Lee",
"Kibok Lee",
"Taeyoung Park"
] | NeurIPS.cc/2024/Conference | 2410.14488 | [
"https://github.com/seunghan96/ant"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1mAaewThcz | @inproceedings{
subramonian2024theoretical,
title={Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks},
author={Arjun Subramonian and Jian Kang and Yizhou Sun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1mAaewThcz}
} | Graph Neural Networks (GNNs) often perform better for high-degree nodes than low-degree nodes on node classification tasks. This degree bias can reinforce social marginalization by, e.g., privileging celebrities and other high-degree actors in social networks during social and content recommendation. While researchers have proposed numerous hypotheses for why GNN degree bias occurs, we find via a survey of 38 degree bias papers that these hypotheses are often not rigorously validated, and can even be contradictory. Thus, we provide an analysis of the origins of degree bias in message-passing GNNs with different graph filters. We prove that high-degree test nodes tend to have a lower probability of misclassification regardless of how GNNs are trained. Moreover, we show that degree bias arises from a variety of factors that are associated with a node's degree (e.g., homophily of neighbors, diversity of neighbors). Furthermore, we show that during training, some GNNs may adjust their loss on low-degree nodes more slowly than on high-degree nodes; however, with sufficiently many epochs of training, message-passing GNNs can achieve their maximum possible training accuracy, which is not significantly limited by their expressive power. Throughout our analysis, we connect our findings to previously-proposed hypotheses for the origins of degree bias, supporting and unifying some while drawing doubt to others. We validate our theoretical findings on 8 common real-world networks, and based on our theoretical and empirical insights, describe a roadmap to alleviate degree bias. | Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks | [
"Arjun Subramonian",
"Jian Kang",
"Yizhou Sun"
] | NeurIPS.cc/2024/Conference | 2404.03139 | [
"https://github.com/arjunsubramonian/degree-bias-exploration"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1l9cEyFmxg | @inproceedings{
hur2024unlocking,
title={Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance},
author={Jiwan Hur and Dong-Jae Lee and Gyojin Han and Jaehyun Choi and Yunho Jeon and Junmo Kim},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1l9cEyFmxg}
} | Masked generative models (MGMs) have shown impressive generative ability while providing an order of magnitude efficient sampling steps compared to continuous diffusion models. However, MGMs still underperform in image synthesis compared to recent well-developed continuous diffusion models with similar size in terms of quality and diversity of generated samples. A key factor in the performance of continuous diffusion models stems from the guidance methods, which enhance the sample quality at the expense of diversity. In this paper, we extend these guidance methods to generalized guidance formulation for MGMs and propose a self-guidance sampling method, which leads to better generation quality. The proposed approach leverages an auxiliary task for semantic smoothing in vector-quantized token space, analogous to the Gaussian blur in continuous pixel space. Equipped with the parameter-efficient fine-tuning method and high-temperature sampling, MGMs with the proposed self-guidance achieve a superior quality-diversity trade-off, outperforming existing sampling methods in MGMs with more efficient training and sampling costs. Extensive experiments with the various sampling hyperparameters confirm the effectiveness of the proposed self-guidance. | Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance | [
"Jiwan Hur",
"Dong-Jae Lee",
"Gyojin Han",
"Jaehyun Choi",
"Yunho Jeon",
"Junmo Kim"
] | NeurIPS.cc/2024/Conference | 2410.13136 | [
"https://github.com/jiwanhur/unlockmgm"
] | https://huggingface.co/papers/2410.13136 | 0 | 0 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=1kyc4TSOFZ | @inproceedings{
gao2024does,
title={Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors},
author={Jiashi Gao and Ziwei Wang and Xiangyu Zhao and Xin Yao and Xuetao Wei},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1kyc4TSOFZ}
} | Federated learning (FL) offers a machine learning paradigm that protects privacy, allowing multiple clients to collaboratively train a global model while only accessing their local data. Recent research in FL has increasingly focused on improving the uniformity of model performance across clients, a fairness principle known as egalitarian fairness. However, achieving egalitarian fairness in FL may sacrifice the model performance for data-rich clients to benefit those with less data. This trade-off raises concerns about the stability of FL, as data-rich clients may opt to leave the current coalition and join another that is more closely aligned with its expected high performance. In this context, our work rigorously addresses the critical concern: **Does egalitarian fairness lead to instability?** Drawing from game theory and social choice theory, we initially characterize fair FL systems as altruism coalition formation games (ACFGs) and reveal that the instability issues emerging from the pursuit of egalitarian fairness are significantly related to the clients’ altruism within the coalition and the configuration of the friends-relationship networks among the clients. Then, we theoretically propose the optimal egalitarian fairness bounds that an FL coalition can achieve while maintaining core stability under various types of altruistic behaviors. The theoretical contributions clarify the quantitative relationships between achievable egalitarian fairness and the disparities in the sizes of local datasets, disproving the misconception that egalitarian fairness inevitably leads to instability. Finally, we conduct experiments to evaluate the consistency of our theoretically derived egalitarian fairness bounds with the empirically achieved egalitarian fairness in fair FL settings. | Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors | [
"Jiashi Gao",
"Ziwei Wang",
"Xiangyu Zhao",
"Xin Yao",
"Xuetao Wei"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=1jG5ngXVs3 | @inproceedings{
zhao2024flowturbo,
title={FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity Refiner},
author={Wenliang Zhao and Minglei Shi and Xumin Yu and Jie Zhou and Jiwen Lu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1jG5ngXVs3}
} | Building on the success of diffusion models in visual generation, flow-based models reemerge as another prominent family of generative models that have achieved competitive or better performance in terms of both visual quality and inference speed. By learning the velocity field through flow-matching, flow-based models tend to produce a straighter sampling trajectory, which is advantageous during the sampling process. However, unlike diffusion models for which fast samplers are well-developed, efficient sampling of flow-based generative models has been rarely explored. In this paper, we propose a framework called FlowTurbo to accelerate the sampling of flow-based models while still enhancing the sampling quality. Our primary observation is that the velocity predictor's outputs in the flow-based models will become stable during the sampling, enabling the estimation of velocity via a lightweight velocity refiner. Additionally, we introduce several techniques including a pseudo corrector and sample-aware compilation to further reduce inference time. Since FlowTurbo does not change the multi-step sampling paradigm, it can be effectively applied for various tasks such as image editing, inpainting, etc. By integrating FlowTurbo into different flow-based models, we obtain an acceleration ratio of 53.1\%$\sim$58.3\% on class-conditional generation and 29.8\%$\sim$38.5\% on text-to-image generation. Notably, FlowTurbo reaches an FID of 2.12 on ImageNet with 100 (ms / img) and FID of 3.93 with 38 (ms / img), achieving the real-time image generation and establishing the new state-of-the-art. Code is available at https://github.com/shiml20/FlowTurbo. | FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity Refiner | [
"Wenliang Zhao",
"Minglei Shi",
"Xumin Yu",
"Jie Zhou",
"Jiwen Lu"
] | NeurIPS.cc/2024/Conference | 2409.18128 | [
"https://github.com/shiml20/flowturbo"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1iHmhMHNyA | @inproceedings{
wang2024large,
title={Large Language Models as Urban Residents: An {LLM} Agent Framework for Personal Mobility Generation},
author={Jiawei Wang and Renhe Jiang and Chuang Yang and Zengqing Wu and Makoto Onizuka and Ryosuke Shibasaki and Noboru Koshizuka and Chuan Xiao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1iHmhMHNyA}
} | This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks. Our approach addresses three research questions: aligning LLMs with real-world urban mobility data, developing reliable activity generation strategies, and exploring LLM applications in urban mobility. The key technical contribution is a novel LLM agent framework that accounts for individual activity patterns and motivations, including a self-consistency approach to align LLMs with real-world activity data and a retrieval-augmented strategy for interpretable activity generation. We evaluate our LLM agent framework and compare it with state-of-the-art personal mobility generation approaches, demonstrating the effectiveness of our approach and its potential applications in urban mobility. Overall, this study marks the pioneering work of designing an LLM agent framework for activity generation based on real-world human activity data, offering a promising tool for urban mobility analysis. | Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation | [
"Jiawei Wang",
"Renhe Jiang",
"Chuang Yang",
"Zengqing Wu",
"Makoto Onizuka",
"Ryosuke Shibasaki",
"Noboru Koshizuka",
"Chuan Xiao"
] | NeurIPS.cc/2024/Conference | 2402.14744 | [
"https://github.com/wangjw6/llmob"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1f82rnwCbl | @inproceedings{
jin2024learning,
title={Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf},
author={Xuanfa Jin and Ziyan Wang and Yali Du and Meng Fang and Haifeng Zhang and Jun Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1f82rnwCbl}
} | Communication is a fundamental aspect of human society, facilitating the exchange of information and beliefs among people. Despite the advancements in large language models (LLMs), recent agents built with these often neglect the control over discussion tactics, which are essential in communication scenarios and games. As a variant of the famous communication game Werewolf, *One Night Ultimate Werewolf* (ONUW) requires players to develop strategic discussion policies due to the potential role changes that increase the uncertainty and complexity of the game. In this work, we first present the existence of the Perfect Bayesian Equilibria (PBEs) in two scenarios of the ONUW game: one with discussion and one without. The results showcase that the discussion greatly changes players' utilities by affecting their beliefs, emphasizing the significance of discussion tactics. Based on the insights obtained from the analyses, we propose an RL-instructed language agent framework, where a discussion policy trained by reinforcement learning (RL) is employed to determine appropriate discussion tactics to adopt. Our experimental results on several ONUW game settings demonstrate the effectiveness and generalizability of our proposed framework. | Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf | [
"Xuanfa Jin",
"Ziyan Wang",
"Yali Du",
"Meng Fang",
"Haifeng Zhang",
"Jun Wang"
] | NeurIPS.cc/2024/Conference | 2405.19946 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1e3MOwHSIX | @inproceedings{
ahia2024magnet,
title={{MAGNET}: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization},
author={Orevaoghene Ahia and Sachin Kumar and Hila Gonen and Valentin Hofmann and Tomasz Limisiewicz and Yulia Tsvetkov and Noah A. Smith},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1e3MOwHSIX}
} | In multilingual settings, non-Latin scripts and low-resource languages are usually disadvantaged in terms of language models’ utility, efficiency, and cost. Specifically, previous studies have reported multiple modeling biases that the current tokenization algorithms introduce to non-Latin script languages, the main one being over-segmentation. In this work, we propose MAGNET— multilingual adaptive gradient-based tokenization—to reduce over-segmentation via adaptive gradient-based subword tokenization. MAGNET learns to predict segment boundaries between byte tokens in a sequence via sub-modules within the model, which act as internal boundary predictors (tokenizers). Previous gradient-based tokenization methods aimed for uniform compression across sequences by integrating a single boundary predictor during training and optimizing it end-to-end through stochastic reparameterization alongside the next token prediction objective. However, this approach still results in over-segmentation for non-Latin script languages in multilingual settings. In contrast, MAGNET offers a customizable architecture where byte-level sequences are routed through language-script-specific predictors, each optimized for its respective language script. This modularity enforces equitable segmentation granularity across different language scripts compared to previous methods. Through extensive experiments, we demonstrate that in addition to reducing segmentation disparities, MAGNET also enables faster language modeling and improves downstream utility. | MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization | [
"Orevaoghene Ahia",
"Sachin Kumar",
"Hila Gonen",
"Valentin Hofmann",
"Tomasz Limisiewicz",
"Yulia Tsvetkov",
"Noah A. Smith"
] | NeurIPS.cc/2024/Conference | 2407.08818 | [
""
] | https://huggingface.co/papers/2407.08818 | 1 | 0 | 1 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=1dpmeH6IHa | @inproceedings{
ma2024iebench,
title={I2{EB}ench: A Comprehensive Benchmark for Instruction-based Image Editing},
author={Yiwei Ma and Jiayi Ji and Ke Ye and Weihuang Lin and zhibin wang and Yonghan Zheng and Qiang Zhou and Xiaoshuai Sun and Rongrong Ji},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1dpmeH6IHa}
} | Significant progress has been made in the field of Instruction-based Image Editing (IIE). However, evaluating these models poses a significant challenge. A crucial requirement in this field is the establishment of a comprehensive evaluation benchmark for accurately assessing editing results and providing valuable insights for its further development. In response to this need, we propose I2EBench, a comprehensive benchmark designed to automatically evaluate the quality of edited images produced by IIE models from multiple dimensions. I2EBench consists of 2,000+ images for editing, along with 4,000+ corresponding original and diverse instructions. It offers three distinctive characteristics: 1) Comprehensive Evaluation Dimensions: I2EBench comprises 16 evaluation dimensions that cover both high-level and low-level aspects, providing a comprehensive assessment of each IIE model. 2) Human Perception Alignment: To ensure the alignment of our benchmark with human perception, we conducted an extensive user study for each evaluation dimension. 3) Valuable Research Insights: By analyzing the advantages and disadvantages of existing IIE models across the 16 dimensions, we offer valuable research insights to guide future development in the field. We will open-source I2EBench, including all instructions, input images, human annotations, edited images from all evaluated methods, and a simple script for evaluating the results from new IIE models. The code, dataset, and generated images from all IIE models are provided in GitHub: https://github.com/cocoshe/I2EBench. | I2EBench: A Comprehensive Benchmark for Instruction-based Image Editing | [
"Yiwei Ma",
"Jiayi Ji",
"Ke Ye",
"Weihuang Lin",
"zhibin wang",
"Yonghan Zheng",
"Qiang Zhou",
"Xiaoshuai Sun",
"Rongrong Ji"
] | NeurIPS.cc/2024/Conference | 2408.14180 | [
"https://github.com/cocoshe/i2ebench"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1cXdndzkxU | @inproceedings{
kim2024an,
title={An Adaptive Approach for Infinitely Many-armed Bandits under Generalized Rotting Constraints},
author={Jung-hun Kim and Milan Vojnovic and Se-Young Yun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1cXdndzkxU}
} | In this study, we consider the infinitely many-armed bandit problems in a rested rotting setting, where the mean reward of an arm may decrease with each pull, while otherwise, it remains unchanged. We explore two scenarios regarding the rotting of rewards: one in which the cumulative amount of rotting is bounded by $V_T$, referred to as the slow-rotting case, and the other in which the cumulative number of rotting instances is bounded by $S_T$, referred to as the abrupt-rotting case. To address the challenge posed by rotting rewards, we introduce an algorithm that utilizes UCB with an adaptive sliding window, designed to manage the bias and variance trade-off arising due to rotting rewards. Our proposed algorithm achieves tight regret bounds for both slow and abrupt rotting scenarios. Lastly, we demonstrate the performance of our algorithm using numerical experiments. | An Adaptive Approach for Infinitely Many-armed Bandits under Generalized Rotting Constraints | [
"Jung-hun Kim",
"Milan Vojnovic",
"Se-Young Yun"
] | NeurIPS.cc/2024/Conference | 2404.14202 | [
"https://github.com/junghunkim7786/an-adaptive-approach-for-infinitely-many-armed-bandits-under-generalized-rotting-constraints"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1c9XHlHTs7 | @inproceedings{
cassel2024warmup,
title={Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes},
author={Asaf Cassel and Aviv Rosenberg},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1c9XHlHTs7}
} | Policy Optimization (PO) methods are among the most popular Reinforcement Learning (RL) algorithms in practice. Recently, Sherman et al. [2023a] proposed a PO-based algorithm with rate-optimal regret guarantees under the linear Markov Decision Process (MDP) model. However, their algorithm relies on a costly pure exploration warm-up phase that is hard to implement in practice. This paper eliminates this undesired warm-up phase, replacing it with a simple and efficient contraction mechanism. Our PO algorithm achieves rate-optimal regret with improved dependence on the other parameters of the problem (horizon and function approximation dimension) in two fundamental settings: adversarial losses with full-information feedback and stochastic losses with bandit feedback. | Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes | [
"Asaf Cassel",
"Aviv Rosenberg"
] | NeurIPS.cc/2024/Conference | 2407.03065 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1YGgaouVgZ | @inproceedings{
kumano2024wide,
title={Wide Two-Layer Networks can Learn from Adversarial Perturbations},
author={Soichiro Kumano and Hiroshi Kera and Toshihiko Yamasaki},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1YGgaouVgZ}
} | Adversarial examples have raised several open questions, such as why they can deceive classifiers and transfer between different models. A prevailing hypothesis to explain these phenomena suggests that adversarial perturbations appear as random noise but contain class-specific features. This hypothesis is supported by the success of perturbation learning, where classifiers trained solely on adversarial examples and the corresponding incorrect labels generalize well to correctly labeled test data. Although this hypothesis and perturbation learning are effective in explaining intriguing properties of adversarial examples, their solid theoretical foundation is limited. In this study, we theoretically explain the counterintuitive success of perturbation learning. We assume wide two-layer networks and the results hold for any data distribution. We prove that adversarial perturbations contain sufficient class-specific features for networks to generalize from them. Moreover, the predictions of classifiers trained on mislabeled adversarial examples coincide with those of classifiers trained on correctly labeled clean samples. The code is available at https://github.com/s-kumano/perturbation-learning. | Wide Two-Layer Networks can Learn from Adversarial Perturbations | [
"Soichiro Kumano",
"Hiroshi Kera",
"Toshihiko Yamasaki"
] | NeurIPS.cc/2024/Conference | 2410.23677 | [
"https://github.com/s-kumano/perturbation-learning"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1WtEqReCyS | @inproceedings{
nguyen2024multilingual,
title={Multilingual Diversity Improves Vision-Language Representations},
author={Thao Nguyen and Matthew Wallingford and Sebastin Santy and Wei-Chiu Ma and Sewoong Oh and Ludwig Schmidt and Pang Wei Koh and Ranjay Krishna},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1WtEqReCyS}
} | Massive web-crawled image-text datasets lay the foundation for recent progress in multimodal learning. These datasets are designed with the goal of training a model to do well on standard computer vision benchmarks, many of which, however, have been shown to be English-centric (e.g., ImageNet). Consequently, existing data curation techniques gravitate towards using predominantly English image-text pairs and discard many potentially useful non-English samples. Our work questions this practice. Multilingual data is inherently enriching not only because it provides a gateway to learn about culturally salient concepts, but also because it depicts common concepts differently from monolingual data. We thus conduct a systematic study to explore the performance benefits of using more samples of non-English origins with respect to English vision tasks. By translating all multilingual image-text pairs from a raw web crawl to English and re-filtering them, we increase the prevalence of (translated) multilingual data in the resulting training set. Pre-training on this dataset outperforms using English-only or English-dominated datasets on ImageNet, ImageNet distribution shifts, image-English-text retrieval and on average across 38 tasks from the DataComp benchmark. On a geographically diverse task like GeoDE, we also observe improvements across all regions, with the biggest gain coming from Africa. In addition, we quantitatively show that English and non-English data are significantly different in both image and (translated) text space. We hope that our findings motivate future work to be more intentional about including multicultural and multilingual data, not just when non-English or geographically diverse tasks are involved, but to enhance model capabilities at large. | Multilingual Diversity Improves Vision-Language Representations | [
"Thao Nguyen",
"Matthew Wallingford",
"Sebastin Santy",
"Wei-Chiu Ma",
"Sewoong Oh",
"Ludwig Schmidt",
"Pang Wei Koh",
"Ranjay Krishna"
] | NeurIPS.cc/2024/Conference | 2405.16915 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=1SmXUGzrH8 | @inproceedings{
zhang2024finestyle,
title={FineStyle: Fine-grained Controllable Style Personalization for Text-to-image Models},
author={Gong Zhang and Kihyuk Sohn and Meera Hahn and Humphrey Shi and Irfan Essa},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1SmXUGzrH8}
} | Few-shot fine-tuning of text-to-image (T2I) generation models enables people to create unique images in their own style using natural languages without requiring extensive prompt engineering. However, fine-tuning with only a handful, as little as one, of image-text paired data prevents fine-grained control of style attributes at generation. In this paper, we present FineStyle, a few-shot fine-tuning method that allows enhanced controllability for style personalized text-to-image generation. To overcome the lack of training data for fine-tuning, we propose a novel concept-oriented data scaling that amplifies the number of image-text pair, each of which focuses on different concepts (e.g., objects) in the style reference image. We also identify the benefit of parameter-efficient adapter tuning of key and value kernels of cross-attention layers. Extensive experiments show the effectiveness of FineStyle at following fine-grained text prompts and delivering visual quality faithful to the specified style, measured by CLIP scores and human raters. | FineStyle: Fine-grained Controllable Style Personalization for Text-to-image Models | [
"Gong Zhang",
"Kihyuk Sohn",
"Meera Hahn",
"Humphrey Shi",
"Irfan Essa"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=1PmsSugB87 | @inproceedings{
neupane2024evidential,
title={Evidential Stochastic Differential Equations for Time-Aware Sequential Recommendation},
author={Krishna Prasad Neupane and Ervine Zheng and Qi Yu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1PmsSugB87}
} | Sequential recommender systems are designed to capture users' evolving interests over time. Existing methods typically assume a uniform time interval among consecutive user interactions and may not capture users' continuously evolving behavior in the short and long term. In reality, the actual time intervals of user interactions vary dramatically. Consequently, as the time interval between interactions increases, so does the uncertainty in user behavior. Intuitively, it is beneficial to establish a correlation between the interaction time interval and the model uncertainty to provide effective recommendations. To this end, we formulate a novel Evidential Neural Stochastic Differential Equation (*E-NSDE*) to seamlessly integrate NSDE and evidential learning for effective time-aware sequential recommendations. The NSDE enables the model to learn users' fine-grained time-evolving behavior by capturing continuous user representation while evidential learning quantifies both aleatoric and epistemic uncertainties considering interaction time interval to provide model confidence during prediction. Furthermore, we derive a mathematical relationship between the interaction time interval and model uncertainty to guide the learning process. Experiments on real-world data demonstrate the effectiveness of the proposed method compared to the SOTA methods. | Evidential Stochastic Differential Equations for Time-Aware Sequential Recommendation | [
"Krishna Prasad Neupane",
"Ervine Zheng",
"Qi Yu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=1PcJ5Evta7 | @inproceedings{
wang2024backdooralign,
title={BackdoorAlign: Mitigating Fine-tuning based Jailbreak Attack with Backdoor Enhanced Safety Alignment},
author={Jiongxiao Wang and Jiazhao Li and Yiquan Li and Xiangyu Qi and Junjie Hu and Yixuan Li and Patrick McDaniel and Muhao Chen and Bo Li and Chaowei Xiao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1PcJ5Evta7}
} | Despite the general capabilities of Large Language Models (LLMs) like GPT-4, these models still request fine-tuning or adaptation with customized data when meeting the specific business demands and intricacies of tailored use cases. However, this process inevitably introduces new safety threats, particularly against the Fine-tuning based Jailbreak Attack (FJAttack) under the setting of Language-Model-as-a-Service (LMaaS), where the model's safety has been significantly compromised by fine-tuning on users' uploaded examples that contain just a few harmful examples. Though potential defenses have been proposed that the service providers of LMaaS can integrate safety examples into the fine-tuning dataset to reduce safety issues, such approaches require incorporating a substantial amount of data, making it inefficient. To effectively defend against the FJAttack with limited safety examples under LMaaS, we propose the Backdoor Enhanced Safety Alignment method inspired by an analogy with the concept of backdoor attacks. In particular, service providers will construct prefixed safety examples with a secret prompt, acting as a "backdoor trigger". By integrating prefixed safety examples into the fine-tuning dataset, the subsequent fine-tuning process effectively acts as the "backdoor attack", establishing a strong correlation between the secret prompt and safety generations. Consequently, safe responses are ensured once service providers prepend this secret prompt ahead of any user input during inference. Our comprehensive experiments demonstrate that through the Backdoor Enhanced Safety Alignment with adding as few as 11 prefixed safety examples, the maliciously fine-tuned LLMs will achieve similar safety performance as the original aligned models without harming the benign performance. Furthermore, we also present the effectiveness of our method in a more practical setting where the fine-tuning data consists of both FJAttack examples and the fine-tuning task data. | BackdoorAlign: Mitigating Fine-tuning based Jailbreak Attack with Backdoor Enhanced Safety Alignment | [
"Jiongxiao Wang",
"Jiazhao Li",
"Yiquan Li",
"Xiangyu Qi",
"Junjie Hu",
"Yixuan Li",
"Patrick McDaniel",
"Muhao Chen",
"Bo Li",
"Chaowei Xiao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=1PNwacZYik | @inproceedings{
zhao2024fastdrag,
title={FastDrag: Manipulate Anything in One Step},
author={Xuanjia Zhao and Jian Guan and Congyi Fan and Dongli Xu and Youtian Lin and Haiwei Pan and Pengming Feng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1PNwacZYik}
} | Drag-based image editing using generative models provides precise control over image contents, enabling users to manipulate anything in an image with a few clicks. However, prevailing methods typically adopt $n$-step iterations for latent semantic optimization to achieve drag-based image editing, which is time-consuming and limits practical applications. In this paper, we introduce a novel one-step drag-based image editing method, i.e., FastDrag, to accelerate the editing process. Central to our approach is a latent warpage function (LWF), which simulates the behavior of a stretched material to adjust the location of individual pixels within the latent space. This innovation achieves one-step latent semantic optimization and hence significantly promotes editing speeds. Meanwhile, null regions emerging after applying LWF are addressed by our proposed bilateral nearest neighbor interpolation (BNNI) strategy. This strategy interpolates these regions using similar features from neighboring areas, thus enhancing semantic integrity. Additionally, a consistency-preserving strategy is introduced to maintain the consistency between the edited and original images by adopting semantic information from the original image, saved as key and value pairs in self-attention module during diffusion inversion, to guide the diffusion sampling. Our FastDrag is validated on the DragBench dataset, demonstrating substantial improvements in processing time over existing methods, while achieving enhanced editing performance. | FastDrag: Manipulate Anything in One Step | [
"Xuanjia Zhao",
"Jian Guan",
"Congyi Fan",
"Dongli Xu",
"Youtian Lin",
"Haiwei Pan",
"Pengming Feng"
] | NeurIPS.cc/2024/Conference | 2405.15769 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1PCsDNG6Jg | @inproceedings{
kalavasis2024on,
title={On the Computational Landscape of Replicable Learning},
author={Alkis Kalavasis and Amin Karbasi and Grigoris Velegkas and Felix Zhou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1PCsDNG6Jg}
} | We study computational aspects of algorithmic replicability, a notion of stability introduced by Impagliazzo, Lei,
Pitassi, and Sorrell [STOC, 2022]. Motivated by a recent line of work that established strong statistical connections between
replicability and other notions of learnability such as online learning, private learning, and SQ learning, we aim to
understand better the computational connections between replicability and these learning paradigms.
Our first result shows that there is a concept class that is efficiently replicably PAC learnable, but, under standard
cryptographic assumptions, no efficient online learner exists for this class. Subsequently, we design an efficient
replicable learner for PAC learning parities when the marginal distribution is far from uniform, making progress on a
question posed by Impagliazzo et al. [STOC, 2022]. To obtain this result, we design a replicable lifting framework inspired by
Blanc, Lange, Malik, and Tan [STOC, 2023], that transforms in a black-box manner efficient replicable PAC learners under the
uniform marginal distribution over the Boolean hypercube to replicable PAC learners under any marginal distribution,
with sample and time complexity that depends on a certain measure of the complexity of the distribution.
Finally, we show that any pure DP learner can be transformed in a black-box manner to a replicable learner, with time complexity polynomial in the confidence and accuracy parameters, but exponential in the representation dimension of the underlying hypothesis class. | On the Computational Landscape of Replicable Learning | [
"Alkis Kalavasis",
"Amin Karbasi",
"Grigoris Velegkas",
"Felix Zhou"
] | NeurIPS.cc/2024/Conference | 2405.15599 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1ONdF1JHyJ | @inproceedings{
duan2024causal,
title={Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model},
author={Yifan Duan and Jian Zhao and pengcheng and Junyuan Mao and Hao Wu and Jingyu Xu and shilong wang and Caoyuan Ma and Kai Wang and Kun Wang and Xuelong Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1ONdF1JHyJ}
} | Spatio-temporal (ST) prediction has garnered a De facto attention in earth sciences, such as meteorological prediction, human mobility perception. However, the scarcity of data coupled with the high expenses involved in sensor deployment results in notable data imbalances. Furthermore, models that are excessively customized and devoid of causal connections further undermine the generalizability and interpretability. To this end, we establish a causal framework for ST predictions, termed CaPaint, which targets to identify causal regions in data and endow model with causal reasoning ability in a two-stage process. Going beyond this process, we utilize the back-door adjustment to specifically address the sub-regions identified as non-causal in the upstream phase. Specifically, we employ a novel image inpainting technique. By using a fine-tuned unconditional Diffusion Probabilistic Model (DDPM) as the generative prior, we in-fill the masks defined as environmental parts, offering the possibility of reliable extrapolation for potential data distributions. CaPaint overcomes the high complexity dilemma of optimal ST causal discovery models by reducing the data generation complexity from exponential to quasi-linear levels. Extensive experiments conducted on five real-world ST benchmarks demonstrate that integrating the CaPaint concept allows models to achieve improvements ranging from 4.3% to 77.3%. Moreover, compared to traditional mainstream ST augmenters, CaPaint underscores the potential of diffusion models in ST enhancement, offering a novel paradigm for this field. Our project is available at https://anonymous.4open.science/r/12345-DFCC. | Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model | [
"Yifan Duan",
"Jian Zhao",
"pengcheng",
"Junyuan Mao",
"Hao Wu",
"Jingyu Xu",
"shilong wang",
"Caoyuan Ma",
"Kai Wang",
"Kun Wang",
"Xuelong Li"
] | NeurIPS.cc/2024/Conference | 2409.19608 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1MCseWaFZb | @inproceedings{
shekarforoush2024cryospin,
title={Cryo{SPIN}: Improving Ab-Initio Cryo-{EM} Reconstruction with Semi-Amortized Pose Inference},
author={Shayan Shekarforoush and David B. Lindell and Marcus A Brubaker and David J. Fleet},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1MCseWaFZb}
} | Cryo-EM is an increasingly popular method for determining the atomic resolution 3D structure of macromolecular complexes (eg, proteins) from noisy 2D images captured by an electron microscope. The computational task is to reconstruct the 3D density of the particle, along with 3D pose of the particle in each 2D image, for which the posterior pose distribution is highly multi-modal. Recent developments in cryo-EM have focused on deep learning for which amortized inference has been used to predict pose. Here, we address key problems with this approach, and propose a new semi-amortized method, cryoSPIN, in which reconstruction begins with amortized inference and then switches to a form of auto-decoding to refine poses locally using stochastic gradient descent. Through evaluation on synthetic datasets, we demonstrate that cryoSPIN is able to handle multi-modal pose distributions during the amortized inference stage, while the later, more flexible stage of direct pose optimization yields faster and more accurate convergence of poses compared to baselines. On experimental data, we show that cryoSPIN outperforms the state-of-the-art cryoAI in speed and reconstruction quality. | CryoSPIN: Improving Ab-Initio Cryo-EM Reconstruction with Semi-Amortized Pose Inference | [
"Shayan Shekarforoush",
"David B. Lindell",
"Marcus A Brubaker",
"David J. Fleet"
] | NeurIPS.cc/2024/Conference | 2406.10455 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1M67AdMBbg | @inproceedings{
hu2024multiview,
title={Multi-view Masked Contrastive Representation Learning for Endoscopic Video Analysis},
author={Kai Hu and Ye Xiao and Yuan Zhang and Xieping Gao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1M67AdMBbg}
} | Endoscopic video analysis can effectively assist clinicians in disease diagnosis and treatment, and has played an indispensable role in clinical medicine. Unlike regular videos, endoscopic video analysis presents unique challenges, including complex camera movements, uneven distribution of lesions, and concealment, and it typically relies on contrastive learning in self-supervised pretraining as its mainstream technique. However, representations obtained from contrastive learning enhance the discriminability of the model but often lack fine-grained information, which is suboptimal in the pixel-level prediction tasks. In this paper, we develop a Multi-view Masked Contrastive Representation Learning (M$^2$CRL) framework for endoscopic video pre-training. Specifically, we propose a multi-view mask strategy for addressing the challenges of endoscopic videos. We utilize the frame-aggregated attention guided tube mask to capture global-level spatiotemporal sensitive representation from the global views, while the random tube mask is employed to focus on local variations from the local views. Subsequently, we combine multi-view mask modeling with contrastive learning to obtain endoscopic video representations that possess fine-grained perception and holistic discriminative capabilities simultaneously. The proposed M$^2$CRL is pre-trained on 7 publicly available endoscopic video datasets and fine-tuned on 3 endoscopic video datasets for 3 downstream tasks. Notably, our M$^2$CRL significantly outperforms the current state-of-the-art self-supervised endoscopic pre-training methods, e.g., Endo-FM (3.5% F1 for classification, 7.5% Dice for segmentation, and 2.2% F1 for detection) and other self-supervised methods, e.g., VideoMAE V2 (4.6% F1 for classification, 0.4% Dice for segmentation, and 2.1% F1 for detection). | Multi-view Masked Contrastive Representation Learning for Endoscopic Video Analysis | [
"Kai Hu",
"Ye Xiao",
"Yuan Zhang",
"Xieping Gao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=1L5vaNIoK5 | @inproceedings{
chen2024diffusion,
title={Diffusion Policy Attacker: Crafting Adversarial Attacks for Diffusion-based Policies},
author={Yipu Chen and Haotian Xue and Yongxin Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1L5vaNIoK5}
} | Diffusion models have emerged as a promising approach for behavior cloning (BC), leveraging their exceptional ability to model multi-modal distributions. Diffusion policies (DP) have elevated BC performance to new heights, demonstrating robust efficacy across diverse tasks, coupled with their inherent flexibility and ease of implementation. Despite the increasing adoption of Diffusion Policies (DP) as a foundation for policy generation, the critical issue of safety remains largely unexplored. While previous attempts have targeted deep policy networks, DP used diffusion models as the policy network, making it ineffective to be attacked using previous methods because of its chained structure and randomness injected. In this paper, we undertake a comprehensive examination of DP safety concerns by introducing adversarial scenarios, encompassing offline and online attacks, global and patch-based attacks. We propose DP-Attacker, a suite of algorithms that can craft effective adversarial attacks across all aforementioned scenarios. We conduct attacks on pre-trained diffusion policies across various manipulation tasks. Through extensive experiments, we demonstrate that DP-Attacker has the capability to significantly decrease the success rate of DP for all scenarios. Particularly in offline scenarios, we exhibit the generation of highly transferable perturbations applicable to all frames. Furthermore, we illustrate the creation of adversarial physical patches that, when applied to the environment, effectively deceive the model. Video results are
put in: https://sites.google.com/view/dp-attacker-videos/. | Diffusion Policy Attacker: Crafting Adversarial Attacks for Diffusion-based Policies | [
"Yipu Chen",
"Haotian Xue",
"Yongxin Chen"
] | NeurIPS.cc/2024/Conference | 2405.19424 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1IU3P8VDbn | @inproceedings{
chi2024unveiling,
title={Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?},
author={Haoang Chi and He Li and Wenjing Yang and Feng Liu and Long Lan and Xiaoguang Ren and Tongliang Liu and Bo Han},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1IU3P8VDbn}
} | Causal reasoning capability is critical in advancing large language models (LLMs) towards artificial general intelligence (AGI). While versatile LLMs appear to have demonstrated capabilities in understanding contextual causality and providing responses that obey the laws of causality, it remains unclear whether they perform genuine causal reasoning akin to humans. However, current evidence indicates the contrary. Specifically, LLMs are only capable of performing shallow (level-1) causal reasoning, primarily attributed to the causal knowledge embedded in their parameters, but they lack the capacity for genuine human-like (level-2) causal reasoning. To support this hypothesis, methodologically, we delve into the autoregression mechanism of transformer-based LLMs, revealing that it is not inherently causal. Empirically, we introduce a new causal Q&A benchmark named CausalProbe 2024, whose corpus is fresh and nearly unseen for the studied LLMs. Empirical results show a significant performance drop on CausalProbe 2024 compared to earlier benchmarks, indicating that LLMs primarily engage in level-1 causal reasoning.To bridge the gap towards level-2 causal reasoning, we draw inspiration from the fact that human reasoning is usually facilitated by general knowledge and intended goals. Inspired by this, we propose G$^2$-Reasoner, a LLM causal reasoning method that incorporates general knowledge and goal-oriented prompts into LLMs' causal reasoning processes. Experiments demonstrate that G$^2$-Reasoner significantly enhances LLMs' causal reasoning capability, particularly in fresh and fictitious contexts. This work sheds light on a new path for LLMs to advance towards genuine causal reasoning, going beyond level-1 and making strides towards level-2. | Unveiling Causal Reasoning in Large Language Models: Reality or Mirage? | [
"Haoang Chi",
"He Li",
"Wenjing Yang",
"Feng Liu",
"Long Lan",
"Xiaoguang Ren",
"Tongliang Liu",
"Bo Han"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=1H2e7USI09 | @inproceedings{
zhang2024flow,
title={Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching},
author={Yasi Zhang and Peiyu Yu and Yaxuan Zhu and Yingshan Chang and Feng Gao and Ying Nian Wu and Oscar Leong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1H2e7USI09}
} | Generative models based on flow matching have attracted significant attention for their simplicity and superior performance in high-resolution image synthesis. By leveraging the instantaneous change-of-variables formula, one can directly compute image likelihoods from a learned flow, making them enticing candidates as priors for downstream tasks such as inverse problems. In particular, a natural approach would be to incorporate such image probabilities in a maximum-a-posteriori (MAP) estimation problem. A major obstacle, however, lies in the slow computation of the log-likelihood, as it requires backpropagating through an ODE solver, which can be prohibitively slow for high-dimensional problems. In this work, we propose an iterative algorithm to approximate the MAP estimator efficiently to solve a variety of linear inverse problems. Our algorithm is mathematically justified by the observation that the MAP objective can be approximated by a sum of $N$ ``local MAP'' objectives, where $N$ is the number of function evaluations. By leveraging Tweedie's formula, we show that we can perform gradient steps to sequentially optimize these objectives. We validate our approach for various linear inverse problems, such as super-resolution, deblurring, inpainting, and compressed sensing, and demonstrate that we can outperform other methods based on flow matching. Code is available at \url{https://github.com/YasminZhang/ICTM}. | Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching | [
"Yasi Zhang",
"Peiyu Yu",
"Yaxuan Zhu",
"Yingshan Chang",
"Feng Gao",
"Ying Nian Wu",
"Oscar Leong"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/yasminzhang/ictm"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=1GpY0hsv2w | @inproceedings{
yu2024trajectory,
title={Trajectory Diffusion for ObjectGoal Navigation},
author={Xinyao Yu and Sixian Zhang and Xinhang Song and Xiaorong Qin and Shuqiang Jiang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1GpY0hsv2w}
} | Object goal navigation requires an agent to navigate to a specified object in an unseen environment based on visual observations and user-specified goals.
Human decision-making in navigation is sequential, planning a most likely sequence of actions toward the goal.
However, existing ObjectNav methods, both end-to-end learning methods and modular methods, rely on single-step planning. They output the next action based on the current model input, which easily overlooks temporal consistency and leads to myopic planning.
To this end, we aim to learn sequence planning for ObjectNav. Specifically, we propose trajectory diffusion to learn the distribution of trajectory sequences conditioned on the current observation and the goal.
We utilize DDPM and automatically collected optimal trajectory segments to train the trajectory diffusion.
Once the trajectory diffusion model is trained, it can generate a temporally coherent sequence of future trajectory for agent based on its current observations.
Experimental results on the Gibson and MP3D datasets demonstrate that the generated trajectories effectively guide the agent, resulting in more accurate and efficient navigation. | Trajectory Diffusion for ObjectGoal Navigation | [
"Xinyao Yu",
"Sixian Zhang",
"Xinhang Song",
"Xiaorong Qin",
"Shuqiang Jiang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=1FikBPewU9 | @inproceedings{
dong2024a,
title={A Global Depth-Range-Free Multi-View Stereo Transformer Network with Pose Embedding},
author={Yitong Dong and Yijin Li and Zhaoyang Huang and Weikang Bian and Jingbo Liu and Hujun Bao and Zhaopeng Cui and Hongsheng Li and Guofeng Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1FikBPewU9}
} | In this paper, we propose a novel multi-view stereo (MVS) framework that gets rid of the depth range prior. Unlike recent prior-free MVS methods that work in a pair-wise manner, our method simultaneously considers all the source images. Specifically, we introduce a Multi-view Disparity Attention (MDA) module to aggregate long-range context information within and across multi-view images. Considering the asymmetry of the epipolar disparity flow, the key to our method lies in accurately modeling multi-view geometric constraints. We integrate pose embedding to encapsulate information such as multi-view camera poses, providing implicit geometric constraints for multi-view disparity feature fusion dominated by attention. Additionally, we construct corresponding hidden states for each source image due to significant differences in the observation quality of the same pixel in the reference frame across multiple source frames. We explicitly estimate the quality of the current pixel corresponding to sampled points on the epipolar line of the source image and dynamically update hidden states through the uncertainty estimation module. Extensive results on the DTU dataset and Tanks\&Temple benchmark demonstrate the effectiveness of our method. | A Global Depth-Range-Free Multi-View Stereo Transformer Network with Pose Embedding | [
"Yitong Dong",
"Yijin Li",
"Zhaoyang Huang",
"Weikang Bian",
"Jingbo Liu",
"Hujun Bao",
"Zhaopeng Cui",
"Hongsheng Li",
"Guofeng Zhang"
] | NeurIPS.cc/2024/Conference | 2411.01893 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1Fc2Xa2cDK | @inproceedings{
b{\"u}rger2024truth,
title={Truth is Universal: Robust Detection of Lies in {LLM}s},
author={Lennart B{\"u}rger and Fred A. Hamprecht and Boaz Nadler},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1Fc2Xa2cDK}
} | Large Language Models (LLMs) have revolutionised natural language processing, exhibiting impressive human-like capabilities. In particular, LLMs are capable of "lying", knowingly outputting false statements. Hence, it is of interest and importance to develop methods to detect when LLMs lie. Indeed, several authors trained classifiers to detect LLM lies based on their internal model activations. However, other researchers showed that these classifiers may fail to generalise, for example to negated statements.
In this work, we aim to develop a robust method to detect when an LLM is lying. To this end, we make the following key contributions: (i) We demonstrate the existence of a two-dimensional subspace, along which the activation vectors of true and false statements can be separated. Notably, this finding is universal and holds for various LLMs, including Gemma-7B, LLaMA2-13B, Mistral-7B and LLaMA3-8B. Our analysis explains the generalisation failures observed in previous studies and sets the stage for more robust lie detection;
(ii) Building upon (i), we construct an accurate LLM lie detector. Empirically, our proposed classifier achieves state-of-the-art performance, attaining 94\% accuracy in both distinguishing true from false factual statements and detecting lies generated in real-world scenarios. | Truth is Universal: Robust Detection of Lies in LLMs | [
"Lennart Bürger",
"Fred A. Hamprecht",
"Boaz Nadler"
] | NeurIPS.cc/2024/Conference | 2407.12831 | [
"https://github.com/sciai-lab/truth_is_universal"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1F32iCJFfa | @inproceedings{
bortoli2024schrodinger,
title={Schrodinger Bridge Flow for Unpaired Data Translation},
author={Valentin De Bortoli and Iryna Korshunova and Andriy Mnih and Arnaud Doucet},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1F32iCJFfa}
} | Mass transport problems arise in many areas of machine learning whereby one wants to compute a map transporting one distribution to another. Generative modeling techniques like Generative Adversarial Networks (GANs) and Denoising Diffusion Models (DMMs) have been successfully adapted to solve such transport problems, resulting in CycleGAN and Bridge Matching respectively. However, these methods do not approximate Optimal Transport (OT) maps, which are known to have desirable properties. Existing techniques approximating OT maps for high-dimensional data-rich problems, including DDMs-based Rectified Flow and Schrodinger bridge procedures, require fully training a DDM-type model at each iteration, or use mini-batch techniques which can introduce significant errors. We propose a novel algorithm to compute the Schrodinger bridge, a dynamic entropy-regularized version of OT, that eliminates the need to train multiple DDMs-like models. This algorithm corresponds to a discretization of a flow of path measures, referred to as the Schrodinger Bridge Flow, whose only stationary point is the Schrodinger bridge. We demonstrate the performance of our algorithm on a variety of unpaired data translation tasks. | Schrodinger Bridge Flow for Unpaired Data Translation | [
"Valentin De Bortoli",
"Iryna Korshunova",
"Andriy Mnih",
"Arnaud Doucet"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=1ELFGSNBGC | @inproceedings{
zhang2024multiview,
title={Multiview Scene Graph},
author={Juexiao Zhang and Gao Zhu and Sihang Li and Xinhao Liu and Haorui Song and Xinran Tang and Chen Feng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1ELFGSNBGC}
} | A proper scene representation is central to the pursuit of spatial intelligence where agents can robustly reconstruct and efficiently understand 3D scenes. A scene representation is either metric, such as landmark maps in 3D reconstruction, 3D bounding boxes in object detection, or voxel grids in occupancy prediction, or topological, such as pose graphs with loop closures in SLAM or visibility graphs in SfM.
In this work, we propose to build Multiview Scene Graphs (MSG) from unposed images, representing a scene topologically with interconnected place and object nodes.
The task of building MSG is challenging for existing representation learning methods since it needs to jointly address both visual place recognition, object detection, and object association from images with limited fields of view and potentially large viewpoint changes.
To evaluate any method tackling this task, we developed an MSG dataset and annotation based on a public 3D dataset.
We also propose an evaluation metric based on the intersection-over-union score of MSG edges.
Moreover, we develop a novel baseline method built on mainstream pretrained vision models, combining visual place recognition and object association into one Transformer decoder architecture.
Experiments demonstrate that our method has superior performance compared to existing relevant baselines. | Multiview Scene Graph | [
"Juexiao Zhang",
"Gao Zhu",
"Sihang Li",
"Xinhao Liu",
"Haorui Song",
"Xinran Tang",
"Chen Feng"
] | NeurIPS.cc/2024/Conference | 2410.11187 | [
"https://github.com/ai4ce/MSG"
] | https://huggingface.co/papers/2410.11187 | 0 | 0 | 0 | 7 | [] | [
"ai4ce/MSG"
] | [] | [] | [
"ai4ce/MSG"
] | [] | 1 | poster |
null | https://openreview.net/forum?id=1Du3mMP5YN | @inproceedings{
zhang2024learning,
title={Learning to Shape In-distribution Feature Space for Out-of-distribution Detection},
author={Yonggang Zhang and Jie Lu and Bo Peng and Zhen Fang and Yiu-ming Cheung},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1Du3mMP5YN}
} | Out-of-distribution (OOD) detection is critical for deploying machine learning models in the open world. To design scoring functions that discern OOD data from the in-distribution (ID) cases from a pre-trained discriminative model, existing methods tend to make rigorous distributional assumptions either explicitly or implicitly due to the lack of knowledge about the learned feature space in advance.
The mismatch between the learned and assumed distributions motivates us to raise a fundamental yet under-explored question: \textit{Is it possible to deterministically model the feature distribution while pre-training a discriminative model?}
This paper gives an affirmative answer to this question by presenting a Distributional Representation Learning (\texttt{DRL}) framework for OOD detection. In particular, \texttt{DRL} explicitly enforces the underlying feature space to conform to a pre-defined mixture distribution, together with an online approximation of normalization constants to enable end-to-end training. Furthermore, we formulate \texttt{DRL} into a provably convergent Expectation-Maximization algorithm to avoid trivial solutions and rearrange the sequential sampling to guide the training consistency. Extensive evaluations across mainstream OOD detection benchmarks empirically manifest the superiority of the proposed \texttt{DRL} over its advanced counterparts. | Learning to Shape In-distribution Feature Space for Out-of-distribution Detection | [
"Yonggang Zhang",
"Jie Lu",
"Bo Peng",
"Zhen Fang",
"Yiu-ming Cheung"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=1BZKqZphsW | @inproceedings{
chaudhary2024riskaverse,
title={Risk-Averse Fine-tuning of Large Language Models},
author={Sapana Chaudhary and Ujwal Dinesha and Dileep Kalathil and Srinivas Shakkottai},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1BZKqZphsW}
} | We consider the challenge of mitigating the generation of negative or toxic content by the Large Language Models (LLMs) in response to certain prompts. We propose integrating risk-averse principles into LLM fine-tuning to minimize the occurrence of harmful outputs, particularly rare but significant events. By optimizing the risk measure of Conditional Value at Risk (CVaR), our methodology trains LLMs to exhibit superior performance in avoiding toxic outputs while maintaining effectiveness in generative tasks. Empirical evaluations on sentiment modification and toxicity mitigation tasks demonstrate the efficacy of risk-averse reinforcement learning with human feedback (RLHF) in promoting a safer and more constructive online discourse environment. | Risk-Averse Fine-tuning of Large Language Models | [
"Sapana Chaudhary",
"Ujwal Dinesha",
"Dileep Kalathil",
"Srinivas Shakkottai"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=1ATLLgvURu | @inproceedings{
benomar2024learningaugmented,
title={Learning-Augmented Priority Queues},
author={Ziyad Benomar and Christian Coester},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1ATLLgvURu}
} | Priority queues are one of the most fundamental and widely used data structures in computer science. Their primary objective is to efficiently support the insertion of new elements with assigned priorities and the extraction of the highest priority element.
In this study, we investigate the design of priority queues within the learning-augmented framework, where algorithms use potentially inaccurate predictions to enhance their worst-case performance.
We examine three prediction models spanning different use cases, and we show how the predictions can be leveraged to enhance the performance of priority queue operations. Moreover, we demonstrate the optimality of our solution and discuss some possible applications. | Learning-Augmented Priority Queues | [
"Ziyad Benomar",
"Christian Coester"
] | NeurIPS.cc/2024/Conference | 2406.04793 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=18RdkSv9h9 | @inproceedings{
babaev2024finally,
title={{FINALLY}: fast and universal speech enhancement with studio-like quality},
author={Nicholas Babaev and Kirill Tamogashev and Azat Saginbaev and Ivan Shchekotov and Hanbin Bae and Hosang Sung and WonJun Lee and Hoon-Young Cho and Pavel Andreev},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=18RdkSv9h9}
} | In this paper, we address the challenge of speech enhancement in real-world recordings, which often contain various forms of distortion, such as background noise, reverberation, and microphone artifacts.
We revisit the use of Generative Adversarial Networks (GANs) for speech enhancement and theoretically show that GANs are naturally inclined to seek the point of maximum density within the conditional clean speech distribution, which, as we argue, is essential for speech enhancement task.
We study various feature extractors for perceptual loss to facilitate the stability of adversarial training, developing a methodology for probing the structure of the feature space.
This leads us to integrate WavLM-based perceptual loss into MS-STFT adversarial training pipeline, creating an effective and stable training procedure for the speech enhancement model.
The resulting speech enhancement model, which we refer to as FINALLY, builds upon the HiFi++ architecture, augmented with a WavLM encoder and a novel training pipeline.
Empirical results on various datasets confirm our model's ability to produce clear, high-quality speech at 48 kHz, achieving state-of-the-art performance in the field of speech enhancement. Demo page: https://samsunglabs.github.io/FINALLY-page/ | FINALLY: fast and universal speech enhancement with studio-like quality | [
"Nicholas Babaev",
"Kirill Tamogashev",
"Azat Saginbaev",
"Ivan Shchekotov",
"Hanbin Bae",
"Hosang Sung",
"WonJun Lee",
"Hoon-Young Cho",
"Pavel Andreev"
] | NeurIPS.cc/2024/Conference | 2410.05920 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=18FGRNd0wZ | @inproceedings{
yan2024invariant,
title={Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation},
author={Keqiang Yan and Xiner Li and Hongyi Ling and Kenna Ashen and Carl Edwards and Raymundo Arroyave and Marinka Zitnik and Heng Ji and Xiaofeng Qian and Xiaoning Qian and Shuiwang Ji},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=18FGRNd0wZ}
} | We consider the problem of crystal materials generation using language models (LMs). A key step is to convert 3D crystal structures into 1D sequences to be processed by LMs. Prior studies used the crystallographic information framework (CIF) file stream, which fails to ensure SE(3) and periodic invariance and may not lead to unique sequence representations for a given crystal structure. Here, we propose a novel method, known as Mat2Seq, to tackle this challenge. Mat2Seq converts 3D crystal structures into 1D sequences and ensures that different mathematical descriptions of the same crystal are represented in a single unique sequence, thereby provably achieving SE(3) and periodic invariance. Experimental results show that, with language models, Mat2Seq achieves promising performance in crystal structure generation as compared with prior methods. | Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation | [
"Keqiang Yan",
"Xiner Li",
"Hongyi Ling",
"Kenna Ashen",
"Carl Edwards",
"Raymundo Arroyave",
"Marinka Zitnik",
"Heng Ji",
"Xiaofeng Qian",
"Xiaoning Qian",
"Shuiwang Ji"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=181llen2gw | @inproceedings{
jung2024a,
title={A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks},
author={Hoin Jung and Taeuk Jang and Xiaoqian Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=181llen2gw}
} | Recent advancements in Vision-Language Models (VLMs) have enabled complex multimodal tasks by processing text and image data simultaneously, significantly enhancing the field of artificial intelligence. However, these models often exhibit biases that can skew outputs towards societal stereotypes, thus necessitating debiasing strategies. Existing debiasing methods focus narrowly on specific modalities or tasks, and require extensive retraining. To address these limitations, this paper introduces Selective Feature Imputation for Debiasing (SFID), a novel methodology that integrates feature pruning and low confidence imputation (LCI) to effectively reduce biases in VLMs. SFID is versatile, maintaining the semantic integrity of outputs and costly effective by eliminating the need for retraining. Our experimental results demonstrate SFID's effectiveness across various VLMs tasks including zero-shot classification, text-to-image retrieval, image captioning, and text-to-image generation, by significantly reducing gender biases without compromising performance. This approach not only enhances the fairness of VLMs applications but also preserves their efficiency and utility across diverse scenarios. | A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks | [
"Hoin Jung",
"Taeuk Jang",
"Xiaoqian Wang"
] | NeurIPS.cc/2024/Conference | 2410.07593 | [
"https://github.com/HoinJung/Unified-Debiaisng-VLM-SFID"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=168NLzTpw8 | @inproceedings{
liang2024unleashing,
title={Unleashing Region Understanding in Intermediate Layers for {MLLM}-based Referring Expression Generation},
author={Yaoyuan Liang and Zhuojun Cai and Jian Xu and Guanbo Huang and Yiran Wang and Xiao Liang and Jiahao Liu and Ziran Li and Jingang Wang and Shao-Lun Huang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=168NLzTpw8}
} | The Multi-modal Large Language Model (MLLM) based Referring Expression Generation (REG) task has gained increasing popularity, which aims to generate an unambiguous text description that applies to exactly one object or region in the image by leveraging foundation models. We empirically found that there exists a potential trade-off between the detailedness and the correctness of the descriptions for the referring objects. On the one hand, generating sentences with more details is usually required in order to provide more precise object descriptions. On the other hand, complicated sentences could easily increase the probability of hallucinations. To address this issue, we propose a training-free framework, named ``unleash-then-eliminate'', which first elicits the latent information in the intermediate layers, and then adopts a cycle-consistency-based decoding method to alleviate the production of hallucinations. Furthermore, to reduce the computational load of cycle-consistency-based decoding, we devise a Probing-based Importance Estimation method to statistically estimate the importance weights of intermediate layers within a subset. These importance weights are then incorporated into the decoding process over the entire dataset, intervening in the next token prediction from intermediate layers.
Extensive experiments conducted on the RefCOCOg and PHD benchmarks show that our proposed framework could outperform existing methods on both semantic and hallucination-related metrics. Code will be made available in https://github.com/Glupayy/unleash-eliminate. | Unleashing Region Understanding in Intermediate Layers for MLLM-based Referring Expression Generation | [
"Yaoyuan Liang",
"Zhuojun Cai",
"Jian Xu",
"Guanbo Huang",
"Yiran Wang",
"Xiao Liang",
"Jiahao Liu",
"Ziran Li",
"Jingang Wang",
"Shao-Lun Huang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=164QnJsYjF | @inproceedings{
hoover2024dense,
title={Dense Associative Memory Through the Lens of Random Features},
author={Benjamin Hoover and Duen Horng Chau and Hendrik Strobelt and Parikshit Ram and Dmitry Krotov},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=164QnJsYjF}
} | Dense Associative Memories are high storage capacity variants of the Hopfield networks that are capable of storing a large number of memory patterns in the weights of the network of a given size. Their common formulations typically require storing each pattern in a separate set of synaptic weights, which leads to the increase of the number of synaptic weights when new patterns are introduced. In this work we propose an alternative formulation of this class of models using random features, commonly used in kernel methods. In this formulation the number of network's parameters remains fixed. At the same time, new memories can be added to the network by modifying existing weights. We show that this novel network closely approximates the energy function and dynamics of conventional Dense Associative Memories and shares their desirable computational properties. | Dense Associative Memory Through the Lens of Random Features | [
"Benjamin Hoover",
"Duen Horng Chau",
"Hendrik Strobelt",
"Parikshit Ram",
"Dmitry Krotov"
] | NeurIPS.cc/2024/Conference | 2410.24153 | [
"https://github.com/bhoov/distributed_DAM"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=15Jm9v7wCo | @inproceedings{
zhang2024noregret,
title={No-Regret Learning for Fair Multi-Agent Social Welfare Optimization},
author={Mengxiao Zhang and Ramiro Deo-Campo Vuong and Haipeng Luo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=15Jm9v7wCo}
} | We consider the problem of online multi-agent Nash social welfare (NSW) maximization. While previous works of Hossain et al. [2021], Jones et al. [2023] study similar problems in stochastic multi-agent multi-armed bandits and show that $\sqrt{T}$-regret is possible after $T$ rounds, their fairness measure is the product of all agents' rewards, instead of their NSW (that is, their geometric mean). Given the fundamental role of NSW in the fairness literature, it is more than natural to ask whether no-regret fair learning with NSW as the objective is possible. In this work, we provide a complete answer to this question in various settings. Specifically, in stochastic $N$-agent $K$-armed bandits, we develop an algorithm with $\widetilde{\mathcal{O}}(K^{\frac{2}{N}}T^{\frac{N-1}{N}})$ regret and prove that the dependence on $T$ is tight, making it a sharp contrast to the $\sqrt{T}$-regret bounds of Hossain et al. [2021], Jones et al. [2023]. We then consider a more challenging version of the problem with adversarial rewards. Somewhat surprisingly, despite NSW being a concave function, we prove that no algorithm can achieve sublinear regret. To circumvent such negative results, we further consider a setting with full-information feedback and design two algorithms with $\sqrt{T}$-regret: the first one has no dependence on $N$ at all and is applicable to not just NSW but a broad class of welfare functions, while the second one has better dependence on $K$ and is preferable when $N$ is small.
Finally, we also show that logarithmic regret is possible whenever there exists one agent who is indifferent about different arms. | No-Regret Learning for Fair Multi-Agent Social Welfare Optimization | [
"Mengxiao Zhang",
"Ramiro Deo-Campo Vuong",
"Haipeng Luo"
] | NeurIPS.cc/2024/Conference | 2405.20678 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=15460JjocO | @inproceedings{
charakorn2024diversity,
title={Diversity Is Not All You Need: Training A Robust Cooperative Agent Needs Specialist Partners},
author={Rujikorn Charakorn and Poramate Manoonpong and Nat Dilokthanakul},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=15460JjocO}
} | Partner diversity is known to be crucial for training a robust generalist cooperative agent. In this paper, we show that partner specialization, in addition to diversity, is crucial for the robustness of a downstream generalist agent. We propose a principled method for quantifying both the diversity and specialization of a partner population based on the concept of mutual information. Then, we observe that the recently proposed cross-play minimization (XP-min) technique produces diverse and specialized partners. However, the generated partners are overfit, reducing their usefulness as training partners. To address this, we propose simple methods, based on reinforcement learning and supervised learning, for extracting the diverse and specialized behaviors of XP-min generated partners but not their overfitness. We demonstrate empirically that the proposed method effectively removes overfitness, and extracted populations produce more robust generalist agents compared to the source XP-min populations. | Diversity Is Not All You Need: Training A Robust Cooperative Agent Needs Specialist Partners | [
"Rujikorn Charakorn",
"Poramate Manoonpong",
"Nat Dilokthanakul"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=14hLJr6kZ3 | @inproceedings{
phan2024enhancing,
title={Enhancing Domain Adaptation through Prompt Gradient Alignment},
author={Hoang Phan and Tung Lam Tran and Quyen Tran and Trung Le},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=14hLJr6kZ3}
} | Prior Unsupervised Domain Adaptation (UDA) methods often aim to train a domain-invariant feature extractor, which may hinder the model from learning sufficiently discriminative features. To tackle this, a line of works based on prompt learning leverages the power of large-scale pre-trained vision-language models to learn both domain-invariant and specific features through a set of domain-agnostic and domain-specific learnable prompts. Those studies typically enforce invariant constraints on representation, output, or prompt space to learn such prompts. Differently, we cast UDA as a multiple-objective optimization problem in which each objective is represented by a domain loss. Under this new framework, we propose aligning per-objective gradients to foster consensus between them. Additionally, to prevent potential overfitting when fine-tuning this deep learning architecture, we penalize the norm of these gradients. To achieve these goals, we devise a practical gradient update procedure that can work under both single-source and multi-source UDA. Empirically, our method consistently surpasses other vision language model adaptation methods by a large margin on a wide range of benchmarks. The implementation is available at https://github.com/VietHoang1512/PGA. | Enhancing Domain Adaptation through Prompt Gradient Alignment | [
"Hoang Phan",
"Tung Lam Tran",
"Quyen Tran",
"Trung Le"
] | NeurIPS.cc/2024/Conference | 2406.09353 | [
"https://github.com/viethoang1512/pga"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=135eKqDoRR | @inproceedings{
cai2024bayesianguided,
title={Bayesian-guided Label Mapping for Visual Reprogramming},
author={Chengyi Cai and Zesheng Ye and Lei Feng and Jianzhong Qi and Feng Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=135eKqDoRR}
} | *Visual reprogramming* (VR) leverages the intrinsic capabilities of pretrained vision models by adapting their input or output interfaces to solve downstream tasks whose labels (i.e., downstream labels) might be totally different from the labels associated with the pretrained models (i.e., pretrained labels).
When adapting the output interface, label mapping methods transform the pretrained labels to downstream labels by establishing a gradient-free one-to-one correspondence between the two sets of labels.
However, in this paper, we reveal that one-to-one mappings may overlook the complex relationship between pretrained and downstream labels. Motivated by this observation, we propose a ***B**ayesian-guided **L**abel **M**apping* (BLM) method.
BLM constructs an iteratively-updated probabilistic label mapping matrix, with each element quantifying a pairwise relationship between pretrained and downstream labels.
The assignment of values to the constructed matrix is guided by Bayesian conditional probability, considering the joint distribution of the downstream labels and the labels predicted by the pretrained model on downstream samples. Experiments conducted on both pretrained vision models (e.g., ResNeXt) and vision-language models (e.g., CLIP) demonstrate the superior performance of BLM over existing label mapping methods. The success of BLM also offers a probabilistic lens through which to understand and analyze the effectiveness of VR.
Our code is available at https://github.com/tmlr-group/BayesianLM. | Bayesian-guided Label Mapping for Visual Reprogramming | [
"Chengyi Cai",
"Zesheng Ye",
"Lei Feng",
"Jianzhong Qi",
"Feng Liu"
] | NeurIPS.cc/2024/Conference | 2410.24018 | [
"https://github.com/tmlr-group/bayesianlm"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=12A1RT1L87 | @inproceedings{
xiao2024are,
title={Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?},
author={Lingao Xiao and Yang He},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=12A1RT1L87}
} | In ImageNet-condensation, the storage for auxiliary soft labels exceeds that of the condensed dataset by over 30 times.
However, are large-scale soft labels necessary for large-scale dataset distillation?
In this paper, we first discover that the high within-class similarity in condensed datasets necessitates the use of large-scale soft labels.
This high within-class similarity can be attributed to the fact that previous methods use samples from different classes to construct a single batch for batch normalization (BN) matching.
To reduce the within-class similarity, we introduce class-wise supervision during the image synthesizing process by batching the samples within classes, instead of across classes.
As a result, we can increase within-class diversity and reduce the size of required soft labels.
A key benefit of improved image diversity is that soft label compression can be achieved through simple random pruning, eliminating the need for complex rule-based strategies. Experiments validate our discoveries.
For example, when condensing ImageNet-1K to 200 images per class, our approach compresses the required soft labels from 113 GB to 2.8 GB (40$\times$ compression) with a 2.6\% performance gain.
Code is available at: https://github.com/he-y/soft-label-pruning-for-dataset-distillation | Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation? | [
"Lingao Xiao",
"Yang He"
] | NeurIPS.cc/2024/Conference | 2410.15919 | [
"https://github.com/he-y/soft-label-pruning-for-dataset-distillation"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=1067784F6e | @inproceedings{
xu2024data,
title={Data Distribution Valuation},
author={Xinyi Xu and Shuaiqi Wang and Chuan-Sheng Foo and Bryan Kian Hsiang Low and Giulia Fanti},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=1067784F6e}
} | Data valuation is a class of techniques for quantitatively assessing the value of data for applications like pricing in data marketplaces. Existing data valuation methods define a value for a discrete dataset. However, in many use cases, users are interested in not only the value of the dataset, but that of the distribution from which the dataset was sampled. For example, consider a buyer trying to evaluate whether to purchase data from different vendors. The buyer may observe (and compare) only a small preview sample from each vendor, to decide which vendor's data distribution is most useful to the buyer and purchase. The core question is how should we compare the values of data distributions from their samples? Under a Huber characterization of the data heterogeneity across vendors, we propose a maximum mean discrepancy (MMD)-based valuation method which enables theoretically principled and actionable policies for comparing data distributions from samples. We empirically demonstrate that our method is sample-efficient and effective in identifying valuable data distributions against several existing baselines, on multiple real-world datasets (e.g., network intrusion detection, credit card fraud detection) and downstream applications (classification, regression). | Data Distribution Valuation | [
"Xinyi Xu",
"Shuaiqi Wang",
"Chuan-Sheng Foo",
"Bryan Kian Hsiang Low",
"Giulia Fanti"
] | NeurIPS.cc/2024/Conference | 2410.04386 | [
"https://github.com/xinyiys/data_distribution_valuation"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=105ZuvpdyW | @inproceedings{
du2024segvol,
title={SegVol: Universal and Interactive Volumetric Medical Image Segmentation},
author={Yuxin Du and Fan BAI and Tiejun Huang and Bo Zhao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=105ZuvpdyW}
} | Precise image segmentation provides clinical study with instructive information. Despite the remarkable progress achieved in medical image segmentation, there is still an absence of a 3D foundation segmentation model that can segment a wide range of anatomical categories with easy user interaction. In this paper, we propose a 3D foundation segmentation model, named SegVol, supporting universal and interactive volumetric medical image segmentation. By scaling up training data to 90K unlabeled Computed Tomography (CT) volumes and 6K labeled CT volumes, this foundation model supports the segmentation of over 200 anatomical categories using semantic and spatial prompts. To facilitate efficient and precise inference on volumetric images, we design a zoom-out-zoom-in mechanism. Extensive experiments on 22 anatomical segmentation tasks verify that SegVol outperforms the competitors in 19 tasks, with improvements up to 37.24\% compared to the runner-up methods. We demonstrate the effectiveness and importance of specific designs by ablation study. We expect this foundation model can promote the development of volumetric medical image analysis. The model and code are publicly available at https://github.com/BAAI-DCAI/SegVol. | SegVol: Universal and Interactive Volumetric Medical Image Segmentation | [
"Yuxin Du",
"Fan BAI",
"Tiejun Huang",
"Bo Zhao"
] | NeurIPS.cc/2024/Conference | 2311.13385 | [
"https://github.com/baai-dcai/segvol"
] | https://huggingface.co/papers/2311.13385 | 1 | 0 | 0 | 4 | [
"BAAI/SegVol",
"yuxindu/segvol"
] | [
"GoodBaiBai88/M3D-Cap",
"GoodBaiBai88/M3D-Seg",
"GoodBaiBai88/M3D-RefSeg"
] | [
"BAAI/SegVol",
"yuxindu/SegVol",
"ImagingDataCommons/SegVolOnIDC",
"DiGuaQiu/MRSegmentator",
"RayOoooo/SegVol"
] | [
"BAAI/SegVol",
"yuxindu/segvol"
] | [
"GoodBaiBai88/M3D-Cap",
"GoodBaiBai88/M3D-Seg",
"GoodBaiBai88/M3D-RefSeg"
] | [
"BAAI/SegVol",
"yuxindu/SegVol",
"ImagingDataCommons/SegVolOnIDC",
"DiGuaQiu/MRSegmentator",
"RayOoooo/SegVol"
] | 1 | oral |
null | https://openreview.net/forum?id=0zfUiSX5si | @inproceedings{
xia2024adanovo,
title={AdaNovo: Towards Robust {\textbackslash}emph\{De Novo\} Peptide Sequencing in Proteomics against Data Biases},
author={Jun Xia and Shaorong Chen and Jingbo Zhou and Xiaojun Shan and Wenjie Du and Zhangyang Gao and Cheng Tan and Bozhen Hu and Jiangbin Zheng and Stan Z. Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0zfUiSX5si}
} | Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the high-throughput analysis of protein composition in biological tissues. Despite the development of several deep learning methods for predicting amino acid sequences (peptides) responsible for generating the observed mass spectra, training data biases hinder further advancements of \emph{de novo} peptide sequencing. Firstly, prior methods struggle to identify amino acids with Post-Translational Modifications (PTMs) due to their lower frequency in training data compared to canonical amino acids, further resulting in unsatisfactory peptide sequencing performance. Secondly, various noise and missing peaks in mass spectra reduce the reliability of training data (Peptide-Spectrum Matches, PSMs). To address these challenges, we propose AdaNovo, a novel and domain knowledge-inspired framework that calculates Conditional Mutual Information (CMI) between the mass spectra and amino acids or peptides, using CMI for robust training against above biases. Extensive experiments indicate that AdaNovo outperforms previous competitors on the widely-used 9-species benchmark, meanwhile yielding 3.6\% - 9.4\% improvements in PTMs identification. The supplements contain the code. | AdaNovo: Towards Robust De Novo Peptide Sequencing in Proteomics against Data Biases | [
"Jun Xia",
"Shaorong Chen",
"Jingbo Zhou",
"Xiaojun Shan",
"Wenjie Du",
"Zhangyang Gao",
"Cheng Tan",
"Bozhen Hu",
"Jiangbin Zheng",
"Stan Z. Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=0zWzJj6lO3 | @inproceedings{
piatti2024cooperate,
title={Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of {LLM} Agents},
author={Giorgio Piatti and Zhijing Jin and Max Kleiman-Weiner and Bernhard Sch{\"o}lkopf and Mrinmaya Sachan and Rada Mihalcea},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0zWzJj6lO3}
} | As AI systems pervade human life, ensuring that large language models (LLMs) make safe decisions remains a significant challenge. We introduce the Governance of the Commons Simulation (GovSim), a generative simulation platform designed to study strategic interactions and cooperative decision-making in LLMs. In GovSim, a society of AI agents must collectively balance exploiting a common resource with sustaining it for future use. This environment enables the study of how ethical considerations, strategic planning, and negotiation skills impact cooperative outcomes. We develop an LLM-based agent architecture and test it with the leading open and closed LLMs. We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%. Ablations reveal that successful multi-agent communication between agents is critical for achieving cooperation in these cases. Furthermore, our analyses show that the failure to achieve sustainable cooperation in most LLMs stems from their inability to formulate and analyze hypotheses about the long-term effects of their actions on the equilibrium of the group. Finally, we show that agents that leverage "Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability. Taken together, GovSim enables us to study the mechanisms that underlie sustainable self-government with specificity and scale. We open source the full suite of our research results, including the simulation environment, agent prompts, and a comprehensive web interface. | Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents | [
"Giorgio Piatti",
"Zhijing Jin",
"Max Kleiman-Weiner",
"Bernhard Schölkopf",
"Mrinmaya Sachan",
"Rada Mihalcea"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/giorgiopiatti/govsim"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=0zFVhMBZHJ | @inproceedings{
antoniak2024mixture,
title={Mixture of Tokens: Continuous MoE through Cross-Example Aggregation},
author={Szymon Antoniak and Micha{\l} Krutul and Maciej Pi{\'o}ro and Jakub Krajewski and Jan Ludziejewski and Kamil Ciebiera and Krystian Kr{\'o}l and Tomasz Odrzyg{\'o}{\'z}d{\'z} and Marek Cygan and Sebastian Jaszczur},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0zFVhMBZHJ}
} | Mixture of Experts (MoE) models based on Transformer architecture are pushing the boundaries of language and vision tasks. The allure of these models lies in their ability to substantially increase the parameter count without a corresponding increase in FLOPs. Most widely adopted MoE models are discontinuous with respect to their parameters - often referred to as *sparse*. At the same time, existing continuous MoE designs either lag behind their sparse counterparts or are incompatible with autoregressive decoding. Motivated by the observation that the adaptation of fully continuous methods has been an overarching trend in Deep Learning, we develop Mixture of Tokens (MoT), a simple, continuous architecture that is capable of scaling the number of parameters similarly to sparse MoE models. Unlike conventional methods, MoT assigns mixtures of tokens from different examples to each expert. This architecture is fully compatible with autoregressive training and generation. Our best models not only achieve a 3x increase in training speed over dense Transformer models in language pretraining but also match the performance of state-of-the-art MoE architectures. Additionally, a close connection between MoT and MoE is demonstrated through a novel technique we call *transition tuning*. | Mixture of Tokens: Continuous MoE through Cross-Example Aggregation | [
"Szymon Antoniak",
"Michał Krutul",
"Maciej Pióro",
"Jakub Krajewski",
"Jan Ludziejewski",
"Kamil Ciebiera",
"Krystian Król",
"Tomasz Odrzygóźdź",
"Marek Cygan",
"Sebastian Jaszczur"
] | NeurIPS.cc/2024/Conference | 2310.15961 | [
"https://github.com/llm-random/llm-random"
] | https://huggingface.co/papers/2310.15961 | 0 | 1 | 0 | 8 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=0uXtFk5KNJ | @inproceedings{
luo2024badam,
title={{BA}dam: A Memory Efficient Full Parameter Optimization Method for Large Language Models},
author={Qijun Luo and Hengxu Yu and Xiao Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0uXtFk5KNJ}
} | This work presents BAdam, an optimization method that leverages the block coordinate descent (BCD) framework with Adam's update rule. BAdam offers a memory efficient approach to the full parameter finetuning of large language models. We conduct
a theoretical convergence analysis for BAdam in the deterministic case. Experimentally, we apply BAdam to finetune the Llama 3-8B and Llama 3-70B models using a single RTX3090-24GB GPU and 4 A100-80GB GPUs, respectively. The results confirm BAdam's efficiency in terms of memory usage, running time, and optimization capability. Furthermore, the downstream performance evaluation based on MT-bench and math benchmarks shows that BAdam outperforms existing memory efficient baselines such as LoRA. It also demonstrates that BAdam can achieve comparable or even superior performance compared to Adam. Finally, the ablation study using SGD's update rule illustrates the suitability of BCD for finetuning LLMs. Our code can be easily integrated into any PyTorch-based codebase and is available at https://github.com/Ledzy/BAdam. | BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models | [
"Qijun Luo",
"Hengxu Yu",
"Xiao Li"
] | NeurIPS.cc/2024/Conference | 2404.02827 | [
"https://github.com/ledzy/badam"
] | https://huggingface.co/papers/2404.02827 | 0 | 0 | 2 | 3 | [
"trollek/danube2-1.8b-SlimOrcaDedup",
"trollek/danube2-1.8b-airoboros-3.2",
"trollek/danube2-1.8b-Neural",
"trollek/danube2-1.8b-SystemChat-1.1",
"trollek/danube2-1.8b-WizardLM-Evol-V2-Unfiltered",
"trollek/danube2-1.8b-glaive-function-calling-v2",
"trollek/danube2-1.8b-CodeFeedback",
"trollek/danube2-1.8b-openhermes",
"sunatte/txt2sql",
"trollek/danube2-1.8b-Code-290k",
"trollek/danube2-1.8b-Tess-v1.5",
"trollek/danube2-1.8b-MathInstruct",
"trollek/danube2-1.8b-Synthia-v1.3",
"trollek/ThoughtStream-4B-v0.1",
"MachoMaheen/devdock4bit"
] | [] | [
"Justinrune/LLaMA-Factory",
"smarttang/blingsec"
] | [
"trollek/danube2-1.8b-SlimOrcaDedup",
"trollek/danube2-1.8b-airoboros-3.2",
"trollek/danube2-1.8b-Neural",
"trollek/danube2-1.8b-SystemChat-1.1",
"trollek/danube2-1.8b-WizardLM-Evol-V2-Unfiltered",
"trollek/danube2-1.8b-glaive-function-calling-v2",
"trollek/danube2-1.8b-CodeFeedback",
"trollek/danube2-1.8b-openhermes",
"sunatte/txt2sql",
"trollek/danube2-1.8b-Code-290k",
"trollek/danube2-1.8b-Tess-v1.5",
"trollek/danube2-1.8b-MathInstruct",
"trollek/danube2-1.8b-Synthia-v1.3",
"trollek/ThoughtStream-4B-v0.1",
"MachoMaheen/devdock4bit"
] | [] | [
"Justinrune/LLaMA-Factory",
"smarttang/blingsec"
] | 1 | poster |
null | https://openreview.net/forum?id=0uGlKYS7a2 | @inproceedings{
assos2024maximizing,
title={Maximizing utility in multi-agent environments by anticipating the behavior of other learners},
author={Angelos Assos and Yuval Dagan and Constantinos Costis Daskalakis},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0uGlKYS7a2}
} | Learning algorithms are often used to make decisions in sequential decision-making environments. In multi-agent settings, the decisions of each agent can affect the utilities/losses of the other agents. Therefore, if an agent is good at anticipating the behavior of the other agents, in particular how they will make decisions in each round as a function of their experience that far, it could try to judiciously make its own decisions over the rounds of the interaction so as to influence the other agents to behave in a way that ultimately benefits its own utility. In this paper, we study repeated two-player games involving two types of agents: a learner, which employs an online learning algorithm to choose its strategy in each round; and an optimizer, which knows the learner's utility function and the learner's online learning algorithm. The optimizer wants to plan ahead to maximize its own utility, while taking into account the learner's behavior. We provide two results: a positive result for repeated zero-sum games and a negative result for repeated general-sum games. Our positive result is an algorithm for the optimizer, which exactly maximizes its utility against a learner that plays the Replicator Dynamics --- the continuous-time analogue of Multiplicative Weights Update (MWU). Additionally, we use this result to provide an algorithm for the optimizer against MWU, i.e.~for the discrete-time setting, which guarantees an average utility for the optimizer that is higher than the value of the one-shot game. Our negative result shows that, unless P=NP, there is no Fully Polynomial Time Approximation Scheme (FPTAS) for maximizing the utility of an optimizer against a learner that best-responds to the history in each round. Yet, this still leaves open the question of whether there exists a polynomial-time algorithm that optimizes the utility up to $o(T)$. | Maximizing utility in multi-agent environments by anticipating the behavior of other learners | [
"Angelos Assos",
"Yuval Dagan",
"Constantinos Costis Daskalakis"
] | NeurIPS.cc/2024/Conference | 2407.04889 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0sycTGl4In | @inproceedings{
kim2024d,
title={4D Gaussian Splatting in the Wild with Uncertainty-Aware Regularization},
author={Mijeong Kim and Jongwoo Lim and Bohyung Han},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0sycTGl4In}
} | Novel view synthesis of dynamic scenes is becoming important in various applications, including augmented and virtual reality.
We propose a novel 4D Gaussian Splatting (4DGS) algorithm for dynamic scenes from casually recorded monocular videos.
To overcome the overfitting problem of existing work for these real-world videos, we introduce an uncertainty-aware regularization that identifies uncertain regions with few observations and selectively imposes additional priors based on diffusion models and depth smoothness on such regions.
This approach improves both the performance of novel view synthesis and the quality of training image reconstruction.
We also identify the initialization problem of 4DGS in fast-moving dynamic regions, where the Structure from Motion (SfM) algorithm fails to provide reliable 3D landmarks.
To initialize Gaussian primitives in such regions, we present a dynamic region densification method using the estimated depth maps and scene flow.
Our experiments show that the proposed method improves the performance of 4DGS reconstruction from a video captured by a handheld monocular camera and also exhibits promising results in few-shot static scene reconstruction. | 4D Gaussian Splatting in the Wild with Uncertainty-Aware Regularization | [
"Mijeong Kim",
"Jongwoo Lim",
"Bohyung Han"
] | NeurIPS.cc/2024/Conference | 2411.08879 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0sJBW05a2W | @inproceedings{
zhang2024d,
title={3D Focusing-and-Matching Network for Multi-Instance Point Cloud Registration},
author={Liyuan Zhang and Le Hui and qi liu and Bo Li and Yuchao Dai},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0sJBW05a2W}
} | Multi-instance point cloud registration aims to estimate the pose of all instances of a model point cloud in the whole scene. Existing methods all adopt the strategy of first obtaining the global correspondence and then clustering to obtain the pose of each instance. However, due to the cluttered and occluded objects in the scene, it is difficult to obtain an accurate correspondence between the model point cloud and all instances in the scene. To this end, we propose a simple yet powerful 3D focusing-and-matching network for multi-instance point cloud registration by learning the multiple pair-wise point cloud registration. Specifically, we first present a 3D multi-object focusing module to locate the center of each object and generate object proposals. By using self-attention and cross-attention to associate the model point cloud with structurally similar objects, we can locate potential matching instances by regressing object centers. Then, we propose a 3D dual-masking instance matching module to estimate the pose between the model point cloud and each object proposal. It performs instance mask and overlap mask masks to accurately predict the pair-wise correspondence. Extensive experiments on two public benchmarks, Scan2CAD and ROBI, show that our method achieves a new state-of-the-art performance on the multi-instance point cloud registration task. | 3D Focusing-and-Matching Network for Multi-Instance Point Cloud Registration | [
"Liyuan Zhang",
"Le Hui",
"qi liu",
"Bo Li",
"Yuchao Dai"
] | NeurIPS.cc/2024/Conference | 2411.07740 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0rl5vWOzRU | @inproceedings{
williams2024scoreoptimal,
title={Score-Optimal Diffusion Schedules},
author={Christopher Williams and Andrew Campbell and Arnaud Doucet and Saifuddin Syed},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0rl5vWOzRU}
} | Denoising diffusion models (DDMs) offer a flexible framework for sampling from high dimensional data distributions. DDMs generate a path of probability distributions interpolating between a reference Gaussian distribution and a data distribution by incrementally injecting noise into the data. To numerically simulate the sampling process, a discretisation schedule from the reference back towards clean data must be chosen. An appropriate discretisation schedule is crucial to obtain high quality samples. However, beyond hand crafted heuristics, a general method for choosing this schedule remains elusive. This paper presents a novel algorithm for adaptively selecting an optimal discretisation schedule with respect to a cost that we derive. Our cost measures the work done by the simulation procedure to transport samples from one point in the diffusion path to the next. Our method does not require hyperparameter tuning and adapts to the dynamics and geometry of the diffusion path. Our algorithm only involves the evaluation of the estimated Stein score, making it scalable to existing pre-trained models at inference time and online during training. We find that our learned schedule recovers performant schedules previously only discovered through manual search and obtains competitive FID scores on image datasets. | Score-Optimal Diffusion Schedules | [
"Christopher Williams",
"Andrew Campbell",
"Arnaud Doucet",
"Saifuddin Syed"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=0qb8KoPsej | @inproceedings{
eberle2024accelerating,
title={Accelerating Matroid Optimization through Fast Imprecise Oracles},
author={Franziska Eberle and Felix Hommelsheim and Alexander Lindermayr and Zhenwei Liu and Nicole Megow and Jens Schl{\"o}ter},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0qb8KoPsej}
} | Querying complex models for precise information (e.g. traffic models, database systems, large ML models) often entails intense computations and results in long response times. Thus, weaker models which give imprecise results quickly can be advantageous, provided inaccuracies can be resolved using few queries to a stronger model. In the fundamental problem of computing a maximum-weight basis of a matroid, a well-known generalization of many combinatorial optimization problems, algorithms have access to a clean oracle to query matroid information. We additionally equip algorithms with a fast but dirty oracle. We design and analyze practical algorithms which only use few clean queries w.r.t. the quality of the dirty oracle, while maintaining robustness against arbitrarily poor dirty oracles, approaching the performance of classic algorithms for the given problem. Notably, we prove that our algorithms are, in many respects, best-possible. Further, we outline extensions to other matroid oracle types, non-free dirty oracles and other matroid problems. | Accelerating Matroid Optimization through Fast Imprecise Oracles | [
"Franziska Eberle",
"Felix Hommelsheim",
"Alexander Lindermayr",
"Zhenwei Liu",
"Nicole Megow",
"Jens Schlöter"
] | NeurIPS.cc/2024/Conference | 2402.02774 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0og7nmvDbe | @inproceedings{
stolfo2024confidence,
title={Confidence Regulation Neurons in Language Models},
author={Alessandro Stolfo and Ben Peng Wu and Wes Gurnee and Yonatan Belinkov and Xingyi Song and Mrinmaya Sachan and Neel Nanda},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0og7nmvDbe}
} | Despite their widespread use, the mechanisms by which large language models (LLMs) represent and regulate uncertainty in next-token predictions remain largely unexplored. This study investigates two critical components believed to influence this uncertainty: the recently discovered entropy neurons and a new set of components that we term token frequency neurons. Entropy neurons are characterized by an unusually high weight norm and influence the final layer normalization (LayerNorm) scale to effectively scale down the logits. Our work shows that entropy neurons operate by writing onto an \textit{unembedding null space}, allowing them to impact the residual stream norm with minimal direct effect on the logits themselves. We observe the presence of entropy neurons across a range of models, up to 7 billion parameters. On the other hand, token frequency neurons, which we discover and describe here for the first time, boost or suppress each token’s logit proportionally to its log frequency, thereby shifting the output distribution towards or away from the unigram distribution. Finally, we present a detailed case study where entropy neurons actively manage confidence: the setting of induction, i.e. detecting and continuing repeated subsequences. | Confidence Regulation Neurons in Language Models | [
"Alessandro Stolfo",
"Ben Peng Wu",
"Wes Gurnee",
"Yonatan Belinkov",
"Xingyi Song",
"Mrinmaya Sachan",
"Neel Nanda"
] | NeurIPS.cc/2024/Conference | 2406.16254 | [
"https://github.com/bpwu1/confidence-regulation-neurons"
] | https://huggingface.co/papers/2406.16254 | 5 | 10 | 1 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=0oUutV92YF | @inproceedings{
hu2024protgo,
title={Prot{GO}: Function-Guided Protein Modeling for Unified Representation Learning},
author={Bozhen Hu and Cheng Tan and Yongjie Xu and Zhangyang Gao and Jun Xia and Lirong Wu and Stan Z. Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0oUutV92YF}
} | Protein representation learning is indispensable for various downstream applications of artificial intelligence for bio-medicine research, such as drug design and function prediction. However, achieving effective representation learning for proteins poses challenges due to the diversity of data modalities involved, including sequence, structure, and function annotations. Despite the impressive capabilities of large language models in biomedical text modelling, there remains a pressing need for a framework that seamlessly integrates these diverse modalities, particularly focusing on the three critical aspects of protein information: sequence, structure, and function. Moreover, addressing the inherent data scale differences among these modalities is essential. To tackle these challenges, we introduce ProtGO, a unified model that harnesses a teacher network equipped with a customized graph neural network (GNN) and a Gene Ontology (GO) encoder to learn hybrid embeddings. Notably, our approach eliminates the need for additional functions as input for the student network, which shares the same GNN module. Importantly, we utilize a domain adaptation method to facilitate distribution approximation for guiding the training of the teacher-student framework. This approach leverages distributions learned from latent representations to avoid the alignment of individual samples. Benchmark experiments highlight that ProtGO significantly outperforms state-of-the-art baselines, clearly demonstrating the advantages of the proposed unified framework. | ProtGO: Function-Guided Protein Modeling for Unified Representation Learning | [
"Bozhen Hu",
"Cheng Tan",
"Yongjie Xu",
"Zhangyang Gao",
"Jun Xia",
"Lirong Wu",
"Stan Z. Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=0o9E8AsFgW | @inproceedings{
zhou2024darksam,
title={Dark{SAM}: Fooling Segment Anything Model to Segment Nothing},
author={Ziqi Zhou and Yufei Song and Minghui Li and Shengshan Hu and Xianlong Wang and Leo Yu Zhang and Dezhong Yao and Hai Jin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0o9E8AsFgW}
} | Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://github.com/CGCL-codes/DarkSAM. | DarkSAM: Fooling Segment Anything Model to Segment Nothing | [
"Ziqi Zhou",
"Yufei Song",
"Minghui Li",
"Shengshan Hu",
"Xianlong Wang",
"Leo Yu Zhang",
"Dezhong Yao",
"Hai Jin"
] | NeurIPS.cc/2024/Conference | 2409.17874 | [
"https://github.com/cgcl-codes/darksam"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0o7Rd5jngV | @inproceedings{
wang2024understanding,
title={Understanding the Expressive Power and Mechanisms of Transformer for Sequence Modeling},
author={Mingze Wang and Weinan E},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0o7Rd5jngV}
} | We conduct a systematic study of the approximation properties of Transformer for sequence modeling with long, sparse and complicated memory.
We investigate the mechanisms through which different components of Transformer, such as the dot-product self-attention, positional encoding and feed-forward layer, affect its expressive power, and we study their combined effects through establishing explicit approximation rates.
Our study reveals the roles of critical parameters in the Transformer, such as the number of layers and the number of attention heads.
These theoretical insights are validated experimentally and offer natural suggestions for alternative architectures. | Understanding the Expressive Power and Mechanisms of Transformer for Sequence Modeling | [
"Mingze Wang",
"Weinan E"
] | NeurIPS.cc/2024/Conference | 2402.00522 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0nzKznCjFG | @inproceedings{
setlur2024private,
title={Private and Personalized Frequency Estimation in a Federated Setting},
author={Amrith Setlur and Vitaly Feldman and Kunal Talwar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0nzKznCjFG}
} | Motivated by the problem of next word prediction on user devices we introduce and study the problem of personalized frequency histogram estimation in a federated setting. In this problem, over some domain, each user observes a number of samples from a distribution which is specific to that user. The goal is to compute for all users a personalized estimate of the user's distribution with error measured in KL divergence. We focus on addressing two central challenges: statistical heterogeneity and protection of user privacy.
Our approach to the problem relies on discovering and exploiting similar subpopulations of users which are often present and latent in real-world data, while minimizing user privacy leakage at the same time. We first present a non-private clustering-based algorithm for the problem, and give a provably joint differentially private version of it with a private data-dependent initialization scheme. Next, we propose a simple data model which is based on a mixture of Dirichlet distributions, to formally motivate our non-private algorithm and demonstrate some properties of its components. Finally, we provide an extensive empirical evaluation of our private and non-private algorithms under varying levels of statistical and size heterogeneity on the Reddit, StackOverflow, and Amazon Reviews datasets. Our results demonstrate significant improvements over standard and clustering-based baselines, and in particular, they show that it is possible to improve over direct personalization of a single global model. | Private and Personalized Frequency Estimation in a Federated Setting | [
"Amrith Setlur",
"Vitaly Feldman",
"Kunal Talwar"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=0nSY8NiILP | @inproceedings{
chierichetti2024tight,
title={Tight Bounds for Learning {RUM}s from Small Slates},
author={Flavio Chierichetti and Mirko Giacchini and Ravi Kumar and Alessandro Panconesi and Andrew Tomkins},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0nSY8NiILP}
} | A Random Utility Model (RUM) is a classical model of user behavior defined by a distribution over $\mathbb{R}^n$. A user, presented with a subset of $\\{1,\ldots,n\\}$, will select the item of the subset with the highest utility, according to a utility vector drawn from the specified distribution. In practical settings, the subset is often of small size, as in the ``ten blue links'' of web search.
In this paper, we consider a learning setting with complete information on user choices from subsets of size at most $k$. We show that $k=\Theta(\sqrt{n})$ is both necessary and sufficient to predict the distribution of all user choices with an arbitrarily small, constant error.
Based on the upper bound, we obtain new algorithms for approximate RUM learning and variations thereof. Furthermore, we employ our lower bound for approximate RUM learning to derive lower bounds to fractional extensions of the well-studied $k$-deck and trace reconstruction problems. | Tight Bounds for Learning RUMs from Small Slates | [
"Flavio Chierichetti",
"Mirko Giacchini",
"Ravi Kumar",
"Alessandro Panconesi",
"Andrew Tomkins"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=0m19blQT6y | @inproceedings{
sui2024bitsfusion,
title={BitsFusion: 1.99 bits Weight Quantization of Diffusion Model},
author={Yang Sui and Yanyu Li and Anil Kag and Yerlan Idelbayev and Junli Cao and Ju Hu and Dhritiman Sagar and Bo Yuan and Sergey Tulyakov and Jian Ren},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0m19blQT6y}
} | Diffusion-based image generation models have achieved great success in recent years by showing the capability of synthesizing high-quality content. However, these models contain a huge number of parameters, resulting in a significantly large model size. Saving and transferring them is a major bottleneck for various applications, especially those running on resource-constrained devices. In this work, we develop a novel weight quantization method that quantizes the UNet from Stable Diffusion v1.5 to $1.99$ bits, achieving a model with $7.9\times$ smaller size while exhibiting even better generation quality than the original one. Our approach includes several novel techniques, such as assigning optimal bits to each layer, initializing the quantized model for better performance, and improving the training strategy to dramatically reduce quantization error. Furthermore, we extensively evaluate our quantized model across various benchmark datasets and through human evaluation to demonstrate its superior generation quality. | BitsFusion: 1.99 bits Weight Quantization of Diffusion Model | [
"Yang Sui",
"Yanyu Li",
"Anil Kag",
"Yerlan Idelbayev",
"Junli Cao",
"Ju Hu",
"Dhritiman Sagar",
"Bo Yuan",
"Sergey Tulyakov",
"Jian Ren"
] | NeurIPS.cc/2024/Conference | 2406.04333 | [
"https://github.com/huggingface/diffusers"
] | https://huggingface.co/papers/2406.04333 | 5 | 36 | 2 | 10 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=0lau89u4oE | @inproceedings{
si2024accelerating,
title={Accelerating Non-Maximum Suppression: A Graph Theory Perspective},
author={King-Siong Si and Lu Sun and Weizhan Zhang and Tieliang Gong and Jiahao Wang and Jiang Liu and Hao Sun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0lau89u4oE}
} | Non-maximum suppression (NMS) is an indispensable post-processing step in object detection. With the continuous optimization of network models, NMS has become the ``last mile'' to enhance the efficiency of object detection. This paper systematically analyzes NMS from a graph theory perspective for the first time, revealing its intrinsic structure. Consequently, we propose two optimization methods, namely QSI-NMS and BOE-NMS. The former is a fast recursive divide-and-conquer algorithm with negligible mAP loss, and its extended version (eQSI-NMS) achieves optimal complexity of $\mathcal{O}(n\log n)$. The latter, concentrating on the locality of NMS, achieves an optimization at a constant level without an mAP loss penalty. Moreover, to facilitate rapid evaluation of NMS methods for researchers, we introduce NMS-Bench, the first benchmark designed to comprehensively assess various NMS methods. Taking the YOLOv8-N model on MS COCO 2017 as the benchmark setup, our method QSI-NMS provides $6.2\times$ speed of original NMS on the benchmark, with a $0.1\%$ decrease in mAP. The optimal eQSI-NMS, with only a $0.3\%$ mAP decrease, achieves $10.7\times$ speed. Meanwhile, BOE-NMS exhibits $5.1\times$ speed with no compromise in mAP. | Accelerating Non-Maximum Suppression: A Graph Theory Perspective | [
"King-Siong Si",
"Lu Sun",
"Weizhan Zhang",
"Tieliang Gong",
"Jiahao Wang",
"Jiang Liu",
"Hao Sun"
] | NeurIPS.cc/2024/Conference | 2409.20520 | [
"https://github.com/Yuri3-xr/NMS-Bench"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0lBx844upd | @inproceedings{
meng2024alps,
title={{ALPS}: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models},
author={Xiang Meng and Kayhan Behdin and Haoyue Wang and Rahul Mazumder},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0lBx844upd}
} | The impressive performance of Large Language Models (LLMs) across various natural language processing tasks comes at the cost of vast computational resources and storage requirements. One-shot pruning techniques offer a way to alleviate these burdens by removing redundant weights without the need for retraining. Yet, the massive scale of LLMs often forces current pruning approaches to rely on heuristics instead of optimization-based techniques, potentially resulting in suboptimal compression. In this paper, we introduce ALPS, an optimization-based framework that tackles the pruning problem using the operator splitting technique and a preconditioned conjugate gradient-based post-processing step. Our approach incorporates novel techniques to accelerate and theoretically guarantee convergence while leveraging vectorization and GPU parallelism for efficiency. ALPS substantially outperforms state-of-the-art methods in terms of the pruning objective and perplexity reduction, particularly for highly sparse models. On the LLaMA3-8B model with 70\% sparsity, ALPS achieves a 29\% reduction in test perplexity on the WikiText dataset and a 8\% improvement in zero-shot benchmark performance compared to existing methods. Our code is available at https://github.com/mazumder-lab/ALPS. | ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models | [
"Xiang Meng",
"Kayhan Behdin",
"Haoyue Wang",
"Rahul Mazumder"
] | NeurIPS.cc/2024/Conference | 2406.07831 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0l9yGPTHAU | @inproceedings{
clavier2024nearoptimal,
title={Near-Optimal Distributionally Robust Reinforcement Learning with General \$L\_p\$ Norms},
author={Pierre Clavier and Laixi Shi and Erwan Le Pennec and Eric Mazumdar and Adam Wierman and Matthieu Geist},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0l9yGPTHAU}
} | To address the challenges of sim-to-real gap and sample efficiency in reinforcement learning (RL), this work studies distributionally robust Markov decision processes (RMDPs) --- optimize the worst-case performance when the deployed environment is within an uncertainty set around some nominal MDP. Despite recent efforts, the sample complexity of RMDPs has remained largely undetermined. While the statistical implications of distributional robustness in RL have been explored in some specific cases, the generalizability of the existing findings remains unclear, especially in comparison to standard RL. Assuming access to a generative model that samples from the nominal MDP, we examine the sample complexity of RMDPs using a class of generalized $L_p$ norms as the 'distance' function for the uncertainty set, under two commonly adopted $sa$-rectangular and $s$-rectangular conditions. Our results imply that RMDPs can be more sample-efficient to solve than standard MDPs using generalized $L_p$ norms in both $sa$- and $s$-rectangular cases, potentially inspiring more empirical research.
We provide a near-optimal upper bound and a matching minimax lower bound for the $sa$-rectangular scenarios. For $s$-rectangular cases, we improve the state-of-the-art upper bound and also derive a lower bound using $L_\infty$ norm that verifies the tightness. | Near-Optimal Distributionally Robust Reinforcement Learning with General L_p Norms | [
"Pierre Clavier",
"Laixi Shi",
"Erwan Le Pennec",
"Eric Mazumdar",
"Adam Wierman",
"Matthieu Geist"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=0jld45XGgJ | @inproceedings{
s{\'u}ken{\'\i}k2024neural,
title={Neural collapse vs. low-rank bias: Is deep neural collapse really optimal?},
author={Peter S{\'u}ken{\'\i}k and Christoph H. Lampert and Marco Mondelli},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0jld45XGgJ}
} | Deep neural networks (DNNs) exhibit a surprising structure in their final layer known as neural collapse (NC), and a growing body of works is currently investigated the propagation of neural collapse to earlier layers of DNNs -- a phenomenon called deep neural collapse (DNC). However, existing theoretical results are restricted to either linear models, the last two layers or binary classification. In contrast, we focus on non-linear models of arbitrary depth in multi-class classification and reveal a surprising qualitative shift. As soon as we go beyond two layers or two classes, DNC stops being optimal for the deep unconstrained features model (DUFM) -- the standard theoretical framework for the analysis of collapse. The main culprit is the low-rank bias of multi-layer regularization schemes. This bias leads to optimal solutions of even lower rank than the neural collapse. We support our theoretical findings with experiments on both DUFM and real data, which show the emergence of the low-rank structure in the solution found by gradient descent. | Neural collapse vs. low-rank bias: Is deep neural collapse really optimal? | [
"Peter Súkeník",
"Christoph H. Lampert",
"Marco Mondelli"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=0feJEykDRx | @inproceedings{
gong2024mobilityllm,
title={Mobility-{LLM}: Learning Visiting Intentions and Travel Preference from Human Mobility Data with Large Language Models},
author={Letian Gong and Yan Lin and Xinyue Zhang and Yiwen Lu and Xuedi Han and Yichen Liu and Shengnan Guo and Youfang Lin and Huaiyu Wan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0feJEykDRx}
} | Location-based services (LBS) have accumulated extensive human mobility data on diverse behaviors through check-in sequences. These sequences offer valuable insights into users’ intentions and preferences. Yet, existing models analyzing check-in sequences fail to consider the semantics contained in these sequences, which closely reflect human visiting intentions and travel preferences, leading to an incomplete comprehension. Drawing inspiration from the exceptional semantic understanding and contextual information processing capabilities of large language models (LLMs) across various domains, we present Mobility-LLM, a novel framework that leverages LLMs to analyze check-in sequences for multiple tasks. Since LLMs cannot directly interpret check-ins, we reprogram these sequences to help LLMs comprehensively understand the semantics of human visiting intentions and travel preferences.
Specifically, we introduce a visiting intention memory network (VIMN) to capture the visiting intentions at each record, along with a shared pool of human travel preference prompts (HTPP) to guide the LLM in understanding users’ travel preferences. These components enhance the model’s ability to extract and leverage semantic information from human mobility data effectively. Extensive experiments on four benchmark datasets and three downstream tasks demonstrate that our approach significantly outperforms existing models, underscoring the effectiveness of Mobility-LLM in advancing our understanding of human mobility data within LBS contexts. | Mobility-LLM: Learning Visiting Intentions and Travel Preference from Human Mobility Data with Large Language Models | [
"Letian Gong",
"Yan Lin",
"Xinyue Zhang",
"Yiwen Lu",
"Xuedi Han",
"Yichen Liu",
"Shengnan Guo",
"Youfang Lin",
"Huaiyu Wan"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=0e5uOaJxo1 | @inproceedings{
hu2024learning,
title={Learning Complete Protein Representation by Dynamically Coupling of Sequence and Structure},
author={Bozhen Hu and Cheng Tan and Jun Xia and Yue Liu and Lirong Wu and Jiangbin Zheng and Yongjie Xu and Yufei Huang and Stan Z. Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0e5uOaJxo1}
} | Learning effective representations is imperative for comprehending proteins and deciphering their biological functions. Recent strides in language models and graph neural networks have empowered protein models to harness primary or tertiary structure information for representation learning. Nevertheless, the absence of practical methodologies to appropriately model intricate inter-dependencies between protein sequences and structures has resulted in embeddings that exhibit low performance on tasks such as protein function prediction. In this study, we introduce CoupleNet, a novel framework designed to interlink protein sequences and structures to derive informative protein representations. CoupleNet integrates multiple levels and scales of features in proteins, encompassing residue identities and positions for sequences, as well as geometric representations for tertiary structures from both local and global perspectives. A two-type dynamic graph is constructed to capture adjacent and distant sequential features and structural geometries, achieving completeness at the amino acid and backbone levels. Additionally, convolutions are executed on nodes and edges simultaneously to generate comprehensive protein embeddings. Experimental results on benchmark datasets showcase that CoupleNet outperforms state-of-the-art methods, exhibiting particularly superior performance in low-sequence similarities scenarios, adeptly identifying infrequently encountered functions and effectively capturing remote homology relationships in proteins. | Learning Complete Protein Representation by Dynamically Coupling of Sequence and Structure | [
"Bozhen Hu",
"Cheng Tan",
"Jun Xia",
"Yue Liu",
"Lirong Wu",
"Jiangbin Zheng",
"Yongjie Xu",
"Yufei Huang",
"Stan Z. Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=0dtA21q83C | @inproceedings{
sreelatha2024denetdm,
title={DeNet{DM}: Debiasing by Network Depth Modulation},
author={Silpa Vadakkeeveetil Sreelatha and Adarsh Kappiyath and Abhra Chaudhuri and Anjan Dutta},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0dtA21q83C}
} | Neural networks trained on biased datasets tend to inadvertently learn spurious correlations, hindering generalization. We formally prove that (1) samples that exhibit spurious correlations lie on a lower rank manifold relative to the ones that do not; and (2) the depth of a network acts as an implicit regularizer on the rank of the attribute subspace that is encoded in its representations. Leveraging these insights, we present DeNetDM, a novel debiasing method that uses network depth modulation as a way of developing robustness to spurious correlations. Using a training paradigm derived from Product of Experts, we create both biased and debiased branches with deep and shallow architectures and then distill knowledge to produce the target debiased model. Our method requires no bias annotations or explicit data augmentation while performing on par with approaches that require either or both. We demonstrate that DeNetDM outperforms existing debiasing techniques on both synthetic and real-world datasets by 5\%. The project page is available at https://vssilpa.github.io/denetdm/. | DeNetDM: Debiasing by Network Depth Modulation | [
"Silpa Vadakkeeveetil Sreelatha",
"Adarsh Kappiyath",
"Abhra Chaudhuri",
"Anjan Dutta"
] | NeurIPS.cc/2024/Conference | 2403.19863 | [
"https://github.com/kadarsh22/denetdm"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0d50Il6enG | @inproceedings{
sinha2024nonparametric,
title={Non-parametric classification via expand-and-sparsify representation},
author={Kaushik Sinha},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0d50Il6enG}
} | In *expand-and-sparsify* (EaS) representation, a data point in $\mathcal{S}^{d-1}$ is first randomly mapped to higher dimension $\mathbb{R}^m$, where $m>d$, followed by a sparsification operation where the informative $k \ll m$ of the $m$ coordinates are set to one and the rest are set to zero. We propose two algorithms for non-parametric classification using such EaS representation. For our first algorithm, we use *winners-take-all* operation for the sparsification step and show that the proposed classifier admits the form of a locally weighted average classifier and establish its consistency via Stone's Theorem. Further, assuming that the conditional probability function $P(y=1|x)=\eta(x)$ is H\"{o}lder continuous and for optimal choice of $m$, we show that the convergence rate of this classifier is minimax-optimal. For our second algorithm, we use *empirical $k$-thresholding* operation for the sparsification step, and under the assumption that data lie on a low dimensional manifold of dimension $d_0\ll d$, we show that the convergence rate of this classifier depends only on $d_0$ and is again minimax-optimal. Empirical evaluations performed on real-world datasets corroborate our theoretical results. | Non-parametric classification via expand-and-sparsify representation | [
"Kaushik Sinha"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=0cgDDa4OFr | @inproceedings{
vetter2024sourcerer,
title={Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation},
author={Julius Vetter and Guy Moss and Cornelius Schr{\"o}der and Richard Gao and Jakob H. Macke},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0cgDDa4OFr}
} | Scientific modeling applications often require estimating a distribution of parameters consistent with a dataset of observations - an inference task also known as source distribution estimation. This problem can be ill-posed, however, since many different source distributions might produce the same distribution of data-consistent simulations. To make a principled choice among many equally valid sources, we propose an approach which targets the maximum entropy distribution, i.e., prioritizes retaining as much uncertainty as possible. Our method is purely sample-based - leveraging the Sliced-Wasserstein distance to measure the discrepancy between the dataset and simulations - and thus suitable for simulators with intractable likelihoods. We benchmark our method on several tasks, and show that it can recover source distributions with substantially higher entropy than recent source estimation methods, without sacrificing the fidelity of the simulations. Finally, to demonstrate the utility of our approach, we infer source distributions for parameters of the Hodgkin-Huxley model from experimental datasets with hundreds of single-neuron measurements. In summary, we propose a principled method for inferring source distributions of scientific simulator parameters while retaining as much uncertainty as possible. | Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation | [
"Julius Vetter",
"Guy Moss",
"Cornelius Schröder",
"Richard Gao",
"Jakob H. Macke"
] | NeurIPS.cc/2024/Conference | 2402.07808 | [
"https://github.com/mackelab/sourcerer"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0cSQ1Sg7db | @inproceedings{
maurer2024generalization,
title={Generalization of Hamiltonian algorithms},
author={Andreas Maurer},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0cSQ1Sg7db}
} | A method to prove generalization results for a class of stochastic learning algorithms is presented. It applies whenever the algorithm generates a distribution, which is absolutely continuous distribution relative to some a-priori measure, and the logarithm of its density is exponentially concentrated about its mean. Applications include bounds for the Gibbs algorithm and randomizations of stable deterministic algorithms, combinations thereof and PAC-Bayesian bounds with data-dependent priors. | Generalization of Hamiltonian algorithms | [
"Andreas Maurer"
] | NeurIPS.cc/2024/Conference | 2405.14469 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0bINeW40u4 | @inproceedings{
ma2024eyegaze,
title={Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning},
author={Chong Ma and Hanqi Jiang and Wenting Chen and Yiwei Li and Zihao Wu and Xiaowei Yu and Zhengliang Liu and Lei Guo and Dajiang Zhu and Tuo Zhang and Dinggang Shen and Tianming Liu and Xiang Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0bINeW40u4}
} | In the medical multi-modal frameworks, the alignment of cross-modality features presents a significant challenge. However, existing works have learned features that are implicitly aligned from the data, without considering the explicit relationships in the medical context. This data-reliance may lead to low generalization of the learned alignment relationships. In this work, we propose the Eye-gaze Guided Multi-modal Alignment (EGMA) framework to harness eye-gaze data for better alignment of medical visual and textual features. We explore the natural auxiliary role of radiologists' eye-gaze data in aligning medical images and text, and introduce a novel approach by using eye-gaze data, collected synchronously by radiologists during diagnostic evaluations. We conduct downstream tasks of image classification and image-text retrieval on four medical datasets, where EGMA achieved state-of-the-art performance and stronger generalization across different datasets. Additionally, we explore the impact of varying amounts of eye-gaze data on model performance, highlighting the feasibility and utility of integrating this auxiliary data into multi-modal alignment framework. | Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning | [
"Chong Ma",
"Hanqi Jiang",
"Wenting Chen",
"Yiwei Li",
"Zihao Wu",
"Xiaowei Yu",
"Zhengliang Liu",
"Lei Guo",
"Dajiang Zhu",
"Tuo Zhang",
"Dinggang Shen",
"Tianming Liu",
"Xiang Li"
] | NeurIPS.cc/2024/Conference | 2403.12416 | [
"https://github.com/momarky/egma"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0bFXbEMz8e | @inproceedings{
sriram2024flowllm,
title={Flow{LLM}: Flow Matching for Material Generation with Large Language Models as Base Distributions},
author={Anuroop Sriram and Benjamin Kurt Miller and Ricky T. Q. Chen and Brandon M Wood},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0bFXbEMz8e}
} | Material discovery is a critical area of research with the potential to revolutionize various fields, including carbon capture, renewable energy, and electronics. However, the immense scale of the chemical space makes it challenging to explore all possible materials experimentally. In this paper, we introduce FlowLLM, a novel generative model that combines large language models (LLMs) and Riemannian flow matching (RFM) to design novel crystalline materials. FlowLLM first fine-tunes an LLM to learn an effective base distribution of meta-stable crystals in a text representation. After converting to a graph representation, the RFM model takes samples from the LLM and iteratively refines the coordinates and lattice parameters. Our approach significantly outperforms state-of-the-art methods, increasing the generation rate of stable materials by over three times and increasing the rate for stable, unique, and novel crystals by $\sim50$% – a huge improvement on a difficult problem. Additionally, the crystals generated by FlowLLM are much closer to their relaxed state when compared with another leading model, significantly reducing post-hoc computational cost. | FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions | [
"Anuroop Sriram",
"Benjamin Kurt Miller",
"Ricky T. Q. Chen",
"Brandon M Wood"
] | NeurIPS.cc/2024/Conference | 2410.23405 | [
"https://github.com/facebookresearch/flowmm"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0aN7VWwp4g | @inproceedings{
yan2024fourier,
title={Fourier Amplitude and Correlation Loss: Beyond Using L2 Loss for Skillful Precipitation Nowcasting},
author={Chiu-Wai Yan and Shi Quan Foo and Van-Hoan Trinh and Dit-Yan Yeung and Ka-Hing Wong and Wai-Kin Wong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0aN7VWwp4g}
} | Deep learning approaches have been widely adopted for precipitation nowcasting in recent years. Previous studies mainly focus on proposing new model architectures to improve pixel-wise metrics. However, they frequently result in blurry predictions which provide limited utility to forecasting operations. In this work, we propose a new Fourier Amplitude and Correlation Loss (FACL) which consists of two novel loss terms: Fourier Amplitude Loss (FAL) and Fourier Correlation Loss (FCL). FAL regularizes the Fourier amplitude of the model prediction and FCL complements the missing phase information. The two loss terms work together to replace the traditional L2 losses such as MSE and weighted MSE for the spatiotemporal prediction problem on signal-based data. Our method is generic, parameter-free and efficient. Extensive experiments using one synthetic dataset and three radar echo datasets demonstrate that our method improves perceptual metrics and meteorology skill scores, with a small trade-off to pixel-wise accuracy and structural similarity. Moreover, to improve the error margin in meteorological skill scores such as Critical Success Index (CSI) and Fractions Skill Score (FSS), we propose and adopt the Regional Histogram Divergence (RHD), a distance metric that considers the patch-wise similarity between signal-based imagery patterns with tolerance to local transforms. | Fourier Amplitude and Correlation Loss: Beyond Using L2 Loss for Skillful Precipitation Nowcasting | [
"Chiu-Wai Yan",
"Shi Quan Foo",
"Van-Hoan Trinh",
"Dit-Yan Yeung",
"Ka-Hing Wong",
"Wai-Kin Wong"
] | NeurIPS.cc/2024/Conference | 2410.23159 | [
"https://github.com/argenycw/facl"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0ZeONp33f0 | @inproceedings{
barlag2024graph,
title={Graph Neural Networks and Arithmetic Circuits},
author={Timon Barlag and Vivian Holzapfel and Laura Strieker and Jonni Virtema and Heribert Vollmer},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0ZeONp33f0}
} | We characterize the computational power of neural networks that follow the graph neural network (GNN) architecture, not restricted to aggregate-combine GNNs or other particular types. We establish an exact correspondence between the expressivity of GNNs using diverse activation functions and arithmetic circuits over real numbers. In our results the activation function of the network becomes a gate type in the circuit. Our result holds for families of constant depth circuits and networks, both uniformly and non-uniformly, for all common activation functions. | Graph Neural Networks and Arithmetic Circuits | [
"Timon Barlag",
"Vivian Holzapfel",
"Laura Strieker",
"Jonni Virtema",
"Heribert Vollmer"
] | NeurIPS.cc/2024/Conference | 2402.17805 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0ZZMUjZJYF | @inproceedings{
ning2024can,
title={Can {LLM}s Learn by Teaching for Better Reasoning? A Preliminary Study},
author={Xuefei Ning and Zifu Wang and Shiyao Li and Zinan Lin and Peiran Yao and Tianyu Fu and Matthew B. Blaschko and Guohao Dai and Huazhong Yang and Yu Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0ZZMUjZJYF}
} | Teaching to improve student models (e.g., knowledge distillation) is an extensively studied methodology in LLMs. However, in human education, teaching enhances not only the students but also the teachers by fostering more rigorous and clearer reasoning, as well as deeper knowledge building. We ask: Can LLMs also learn by teaching (LbT) for better reasoning? If the answer is yes, we can potentially unlock the possibility of continuously advancing the models without solely relying on human-produced data or stronger models. In this paper, we provide a preliminary exploration of this question. We show that LbT ideas can be incorporated into existing LLM training/prompting pipelines and bring improvements. Specifically, we design three methods, each mimicking one of the three levels of LbT: observing students' feedback, learning from the feedback, and learning iteratively, with the goal of improving answer accuracy without training or improving models' inherent capability with fine-tuning. We reveal some findings: (1) Teaching materials that make it easier for students to learn (via in-context learning) have clearer and more accurate logic; (2) Weak-to-strong generalization: LbT might help improve strong models by teaching weak models; (3) Diversity in students might help: teaching multiple students could be better than teaching a single student or the teacher alone. We hope that our exploration can inspire future research on LbT and, more broadly, the adoption of advanced education techniques to improve LLMs. The code and website are at https://github.com/imagination-research/lbt and https://sites.google.com/view/llm-learning-by-teaching. | Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study | [
"Xuefei Ning",
"Zifu Wang",
"Shiyao Li",
"Zinan Lin",
"Peiran Yao",
"Tianyu Fu",
"Matthew B. Blaschko",
"Guohao Dai",
"Huazhong Yang",
"Yu Wang"
] | NeurIPS.cc/2024/Conference | 2406.14629 | [
"https://github.com/imagination-research/lbt"
] | https://huggingface.co/papers/2406.14629 | 6 | 18 | 2 | 10 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=0XeNkkENuI | @inproceedings{
defazio2024the,
title={The Road Less Scheduled},
author={Aaron Defazio and Xingyu Alice Yang and Ahmed Khaled and Konstantin Mishchenko and Harsh Mehta and Ashok Cutkosky},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0XeNkkENuI}
} | Existing learning rate schedules that do not require specification of the optimization stopping step $T$ are greatly out-performed by learning rate schedules that depend on $T$. We propose an approach that avoids the need for this stopping time by eschewing the use of schedules entirely, while exhibiting state-of-the-art performance compared to schedules across a wide family of problems ranging from convex problems to large-scale deep learning problems. Our Schedule-Free approach introduces no additional hyper-parameters over standard optimizers with momentum. Our method is a direct consequence of a new theory we develop that unifies scheduling and iterate averaging. An open source implementation of our method is available at https://github.com/facebookresearch/schedule_free. Schedule-Free AdamW is the core algorithm behind our winning entry to the MLCommons 2024 AlgoPerf Algorithmic Efficiency Challenge Self-Tuning track. | The Road Less Scheduled | [
"Aaron Defazio",
"Xingyu Alice Yang",
"Ahmed Khaled",
"Konstantin Mishchenko",
"Harsh Mehta",
"Ashok Cutkosky"
] | NeurIPS.cc/2024/Conference | 2405.15682 | [
"https://github.com/facebookresearch/schedule_free"
] | https://huggingface.co/papers/2405.15682 | 3 | 20 | 3 | 7 | [] | [] | [] | [] | [] | [] | 1 | oral |
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