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null | https://openreview.net/forum?id=gkOzoHBXUw | @inproceedings{
bai2024federated,
title={Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources},
author={Jiamu Bai and Daoyuan Chen and Bingchen Qian and Liuyi Yao and Yaliang Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gkOzoHBXUw}
} | Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of Large Language Models (LLMs). While promising, it raises significant challenges due to the heterogeneous resources and data distributions of clients.This study introduces FlexLoRA, a simple yet effective aggregation scheme for LLM fine-tuning, which mitigates the "buckets effect" in traditional FL that restricts the potential of clients with ample resources by tying them to the capabilities of the least-resourced participants. FlexLoRA allows for dynamic adjustment of local LoRA ranks, fostering the development of a global model imbued with broader, less task-specific knowledge. By synthesizing a full-size LoRA weight from individual client contributions and employing Singular Value Decomposition (SVD) for weight redistribution, FlexLoRA fully leverages heterogeneous client resources. Involving thousands of clients performing heterogeneous NLP tasks and client resources, our experiments validate the efficacy of FlexLoRA, with the federated global model achieving consistently better improvement over SOTA FL methods in downstream NLP task performance across various heterogeneous distributions. FlexLoRA's practicality is further underscored by our theoretical analysis and its seamless integration with existing LoRA-based FL methods, offering a path toward cross-device, privacy-preserving federated tuning for LLMs. | Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources | [
"Jiamu Bai",
"Daoyuan Chen",
"Bingchen Qian",
"Liuyi Yao",
"Yaliang Li"
] | NeurIPS.cc/2024/Conference | 2402.11505 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gkJ5nBIOU4 | @inproceedings{
gruntkowska2024improving,
title={Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity},
author={Kaja Gruntkowska and Alexander Tyurin and Peter Richt{\'a}rik},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gkJ5nBIOU4}
} | Effective communication between the server and workers plays a key role in distributed optimization. In this paper, we focus on optimizing communication, uncovering inefficiencies in prevalent downlink compression approaches. Considering first the pure setup where the uplink communication costs are negligible, we introduce MARINA-P, a novel method for downlink compression, employing a collection of correlated compressors. Theoretical analysis demonstrates that MARINA-P with permutation compressors can achieve a server-to-worker communication complexity improving with the number of workers, thus being provably superior to existing algorithms. We further show that MARINA-P can serve as a starting point for extensions such as methods supporting bidirectional compression: we introduce M3, a method combining MARINA-P with uplink compression and a momentum step, achieving bidirectional compression with provable improvements in total communication complexity as the number of workers increases. Theoretical findings align closely with empirical experiments, underscoring the efficiency of the proposed algorithms. | Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity | [
"Kaja Gruntkowska",
"Alexander Tyurin",
"Peter Richtárik"
] | NeurIPS.cc/2024/Conference | 2402.06412 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=gjEzL0bamb | @inproceedings{
ye2024mimictalk,
title={MimicTalk: Mimicking a personalized and expressive 3D talking face in minutes},
author={Zhenhui Ye and Tianyun Zhong and Yi Ren and Ziyue Jiang and Jiawei Huang and Rongjie Huang and Jinglin Liu and Jinzheng He and Chen Zhang and Zehan Wang and Xize Cheng and Xiang Yin and Zhou Zhao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gjEzL0bamb}
} | Talking face generation (TFG) aims to animate a target identity's face to create realistic talking videos. Personalized TFG is a variant that emphasizes the perceptual identity similarity of the synthesized result (from the perspective of appearance and talking style). While previous works typically solve this problem by learning an individual neural radiance field (NeRF) for each identity to implicitly store its static and dynamic information, we find it inefficient and non-generalized due to the per-identity-per-training framework and the limited training data. To this end, we propose MimicTalk, the first attempt that exploits the rich knowledge from a NeRF-based person-agnostic generic model for improving the efficiency and robustness of personalized TFG. To be specific, (1) we first come up with a person-agnostic 3D TFG model as the base model and propose to adapt it into a specific identity; (2) we propose a static-dynamic-hybrid adaptation pipeline to help the model learn the personalized static appearance and facial dynamic features; (3) To generate the facial motion of the personalized talking style, we propose an in-context stylized audio-to-motion model that mimics the implicit talking style provided in the reference video without information loss by an explicit style representation. The adaptation process to an unseen identity can be performed in 15 minutes, which is 47 times faster than previous person-dependent methods. Experiments show that our MimicTalk surpasses previous baselines regarding video quality, efficiency, and expressiveness. Video samples are available at https://mimictalk.github.io . | MimicTalk: Mimicking a personalized and expressive 3D talking face in minutes | [
"Zhenhui Ye",
"Tianyun Zhong",
"Yi Ren",
"Ziyue Jiang",
"Jiawei Huang",
"Rongjie Huang",
"Jinglin Liu",
"Jinzheng He",
"Chen Zhang",
"Zehan Wang",
"Xize Cheng",
"Xiang Yin",
"Zhou Zhao"
] | NeurIPS.cc/2024/Conference | 2410.06734 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gipFTlvfF1 | @inproceedings{
jung2024conditional,
title={Conditional Synthesis of 3D Molecules with Time Correction Sampler},
author={Hojung Jung and Youngrok Park and Laura Schmid and Jaehyeong Jo and Dongkyu Lee and Bongsang Kim and Se-Young Yun and Jinwoo Shin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gipFTlvfF1}
} | Diffusion models have demonstrated remarkable success in various domains, including molecular generation. However, conditional molecular generation remains a fundamental challenge due to an intrinsic trade-off between targeting specific chemical properties and generating meaningful samples from the data distribution. In this work, we present Time-Aware Conditional Synthesis (TACS), a novel approach to conditional generation on diffusion models. It integrates adaptively controlled plug-and-play "online" guidance into a diffusion model, driving samples toward the desired properties while maintaining validity and stability. A key component of our algorithm is our new type of diffusion sampler, Time Correction Sampler (TCS), which is used to control guidance and ensure that the generated molecules remain on the correct manifold at each reverse step of the diffusion process at the same time. Our proposed method demonstrates significant performance in conditional 3D molecular generation and offers a promising approach towards inverse molecular design, potentially facilitating advancements in drug discovery, materials science, and other related fields. | Conditional Synthesis of 3D Molecules with Time Correction Sampler | [
"Hojung Jung",
"Youngrok Park",
"Laura Schmid",
"Jaehyeong Jo",
"Dongkyu Lee",
"Bongsang Kim",
"Se-Young Yun",
"Jinwoo Shin"
] | NeurIPS.cc/2024/Conference | 2411.00551 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=giXUx4VH9t | @inproceedings{
kim2024testtime,
title={Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line},
author={Eungyeup Kim and Mingjie Sun and Christina Baek and Aditi Raghunathan and J Zico Kolter},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=giXUx4VH9t}
} | Recently, Miller et al. (2021) and Baek et al. (2022) empirically demonstrated strong linear correlations between in-distribution (ID) versus out-of-distribution (OOD) accuracy and agreement. These trends, coined accuracy-on-the-line (ACL) and agreement-on-the-line (AGL), enables OOD model selection and performance estimation without labeled data. However, these phenomena also break for certain shifts, such as CIFAR10-C Gaussian Noise, posing a critical bottleneck. In this paper, we make a key finding that recent test-time adaptation (TTA) methods not only improve OOD performance, but drastically strengthens the ACL and AGL trends in models, even in shifts where models showed very weak correlations before. To analyze this, we revisit the theoretical conditions established by Miller et al. (2021), which demonstrate that ACL appears if the distributions only shift in mean and covariance scale in Gaussian data. We find that these theoretical conditions hold when deep networks are adapted to OOD, e.g., CIFAR10-C --- models embed the initial data distribution, with complex shifts, into those only with a singular ``scaling'' variable in the feature space. Building on these stronger linear trends, we demonstrate that combining TTA and AGL-based methods can predict the OOD performance with high precision for a broader set of distribution shifts. Furthermore, we can leverage ACL and AGL to perform hyperparameter search and select the best adaptation strategy without any OOD labeled data. | Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line | [
"Eungyeup Kim",
"Mingjie Sun",
"Christina Baek",
"Aditi Raghunathan",
"J Zico Kolter"
] | NeurIPS.cc/2024/Conference | 2310.04941 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gffaYDu9mM | @inproceedings{
hu2024innout,
title={In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment},
author={Dongting Hu and Huan Fu and Jiaxian Guo and Liuhua Peng and Tingjin Chu and Feng Liu and Tongliang Liu and Mingming Gong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gffaYDu9mM}
} | Neural representations for 3D scenes have made substantial advancements recently, yet object removal remains a challenging yet practical issue, due to the absence of multi-view supervision over occluded areas. Diffusion Models (DMs), trained on extensive 2D images, show diverse and high-fidelity generative capabilities in the 2D domain. However, due to not being specifically trained on 3D data, their application to multi-view data often exacerbates inconsistency, hence impacting the overall quality of the 3D output. To address these issues, we introduce "In-N-Out", a novel approach that begins by inpainting a prior, i.e., the occluded area from a single view using DMs, followed by outstretching it to create multi-view inpaintings via latents alignments. Our analysis identifies that the variability in DMs' outputs mainly arises from initially sampled latents and intermediate latents predicted in the denoising process. We explicitly align of initial latents using a Neural Radiance Field (NeRF) to establish a consistent foundational structure in the inpainted area, complemented by an implicit alignment of intermediate latents through cross-view attention during the denoising phases, enhancing appearance consistency across views. To further enhance rendering results, we apply a patch-based hybrid loss to optimize NeRF. We demonstrate that our techniques effectively mitigate the challenges posed by inconsistencies in DMs and substantially improve the fidelity and coherence of inpainted 3D representations. | In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment | [
"Dongting Hu",
"Huan Fu",
"Jiaxian Guo",
"Liuhua Peng",
"Tingjin Chu",
"Feng Liu",
"Tongliang Liu",
"Mingming Gong"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=ge8GZn8Gtu | @inproceedings{
chen2024achieving,
title={Achieving Optimal Clustering in Gaussian Mixture Models with Anisotropic Covariance Structures},
author={Xin Chen and Anderson Ye Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ge8GZn8Gtu}
} | We study clustering under anisotropic Gaussian Mixture Models (GMMs), where covariance matrices from different clusters are unknown and are not necessarily the identity matrix. We analyze two anisotropic scenarios: homogeneous, with identical covariance matrices, and heterogeneous, with distinct matrices per cluster. For these models, we derive minimax lower bounds that illustrate the critical influence of covariance structures on clustering accuracy. To solve the clustering problem, we consider a variant of Lloyd's algorithm, adapted to estimate and utilize covariance information iteratively. We prove that the adjusted algorithm not only achieves the minimax optimality but also converges within a logarithmic number of iterations, thus bridging the gap between theoretical guarantees and practical efficiency. | Achieving Optimal Clustering in Gaussian Mixture Models with Anisotropic Covariance Structures | [
"Xin Chen",
"Anderson Ye Zhang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=gcpeEg88R3 | @inproceedings{
huk2024quasibayes,
title={Quasi-Bayes meets Vines},
author={David Huk and Yuanhe Zhang and Ritabrata Dutta and Mark Steel},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gcpeEg88R3}
} | Recently developed quasi-Bayesian (QB) methods \cite{fong2023martingale} proposed a stimulating change of paradigm in Bayesian computation by directly constructing the Bayesian predictive distribution through recursion, removing the need for expensive computations involved in sampling the Bayesian posterior distribution. This has proved to be data-efficient for univariate predictions, however, existing constructions for higher dimensional densities are only possible by relying on restrictive assumptions on the model's multivariate structure. Here, we propose a wholly different approach to extend Quasi-Bayesian prediction to high dimensions through the use of Sklar's theorem, by decomposing the predictive distribution into one-dimensional predictive marginals and a high-dimensional copula. We use the efficient recursive QB construction for the one-dimensional marginals and model the dependence using highly expressive vine copulas. Further, we tune hyperparameters using robust divergences (eg. energy score) and show that our proposed Quasi-Bayesian Vine (QB-Vine) is a fully non-parametric density estimator with \emph{an analytical form} and convergence rate independent of the dimension of the data in some situations. Our experiments illustrate that the QB-Vine is appropriate for high dimensional distributions ($\sim$64), needs very few samples to train ($\sim$200) and outperforms state-of-the-art methods with analytical forms for density estimation and supervised tasks by a considerable margin. | Quasi-Bayes meets Vines | [
"David Huk",
"Yuanhe Zhang",
"Ritabrata Dutta",
"Mark Steel"
] | NeurIPS.cc/2024/Conference | 2406.12764 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gZWYdJ3c26 | @inproceedings{
jang2024talos,
title={{TAL}oS: Enhancing Semantic Scene Completion via Test-time Adaptation on the Line of Sight},
author={Hyun-Kurl Jang and Jihun Kim and Hyeokjun Kweon and Kuk-Jin Yoon},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gZWYdJ3c26}
} | Semantic Scene Completion (SSC) aims to perform geometric completion and semantic segmentation simultaneously. Despite the promising results achieved by existing studies, the inherently ill-posed nature of the task presents significant challenges in diverse driving scenarios. This paper introduces TALoS, a novel test-time adaptation approach for SSC that excavates the information available in driving environments. Specifically, we focus on that observations made at a certain moment can serve as Ground Truth (GT) for scene completion at another moment. Given the characteristics of the LiDAR sensor, an observation of an object at a certain location confirms both 1) the occupation of that location and 2) the absence of obstacles along the line of sight from the LiDAR to that point. TALoS utilizes these observations to obtain self-supervision about occupancy and emptiness, guiding the model to adapt to the scene in test time. In a similar manner, we aggregate reliable SSC predictions among multiple moments and leverage them as semantic pseudo-GT for adaptation. Further, to leverage future observations that are not accessible at the current time, we present a dual optimization scheme using the model in which the update is delayed until the future observation is available. Evaluations on the SemanticKITTI validation and test sets demonstrate that TALoS significantly improves the performance of the pre-trained SSC model. | TALoS: Enhancing Semantic Scene Completion via Test-time Adaptation on the Line of Sight | [
"Hyun-Kurl Jang",
"Jihun Kim",
"Hyeokjun Kweon",
"Kuk-Jin Yoon"
] | NeurIPS.cc/2024/Conference | 2410.15674 | [
"https://github.com/blue-531/talos"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gYjM1BZzdX | @inproceedings{
carri{\`e}re2024diffeomorphic,
title={Diffeomorphic interpolation for efficient persistence-based topological optimization},
author={Mathieu Carri{\`e}re and Marc Theveneau and Th{\'e}o Lacombe},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gYjM1BZzdX}
} | Topological Data Analysis (TDA) provides a pipeline to extract quantitative and powerful topological descriptors from structured objects.
This enables the definition of topological loss functions, which assert to which extent a given object exhibits some topological properties.
One can then use these losses to perform topological optimization via gradient descent routines.
While theoretically sounded, topological optimization faces an important challenge: gradients tend to be extremely sparse, in the sense that the loss function typically depends (locally) on only very few coordinates of the input object, yielding dramatically slow optimization schemes in practice.
In this work, focusing on the central case of topological optimization for point clouds, we propose to overcome this limitation using diffeomorphic interpolation, turning sparse gradients into smooth vector fields defined on the whole space.
In particular, this approach combines efficiently with subsampling techniques routinely used in TDA, as the diffeomorphism derived from the gradient computed on the subsample can be used to update the coordinates of the full and possibly large input object. We then illustrate the usefulness of our approach on black-box autoencoder (AE) regularization, where we aim at applying some topological priors on the latent spaces associated to fixed, black-box AE models without modifying their (unknown) architectures and parameters. We empirically show that, while vanilla topological optimization has to be re-run every time that new data comes out of the black-box models, learning a diffeomorphic flow can be done once and then re-applied to new data in linear time. Moreover, reverting the flow allows us to generate data by sampling the topologically-optimized latent space directly, allowing for better interpretability of the model. | Diffeomorphic interpolation for efficient persistence-based topological optimization | [
"Mathieu Carrière",
"Marc Theveneau",
"Théo Lacombe"
] | NeurIPS.cc/2024/Conference | 2405.18820 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gYa94o5Gmq | @inproceedings{
yu2024towards,
title={Towards Unsupervised Model Selection for Domain Adaptive Object Detection},
author={Hengfu Yu and Jinhong Deng and Wen Li and Lixin Duan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gYa94o5Gmq}
} | Evaluating the performance of deep models in new scenarios has drawn increasing attention in recent years due to the wide application of deep learning techniques in various fields. However, while it is possible to collect data from new scenarios, the annotations are not always available. Existing Domain Adaptive Object Detection (DAOD) works usually report their performance by selecting the best model on the validation set or even the test set of the target domain, which is highly impractical in real-world applications. In this paper, we propose a novel unsupervised model selection approach for domain adaptive object detection, which is able to select almost the optimal model for the target domain without using any target labels. Our approach is based on the flat minima principle, i.e., models located in the flat minima region in the parameter space usually exhibit excellent generalization ability. However, traditional methods require labeled data to evaluate how well a model is located in the flat minima region, which is unrealistic for the DAOD task. Therefore, we design a Detection Adaptation Score (DAS) approach to approximately measure the flat minima without using target labels. We show via a generalization bound that the flatness can be deemed as model variance, while the minima depend on the domain distribution distance for the DAOD task. Accordingly, we propose a Flatness Index Score (FIS) to assess the flatness by measuring the classification and localization fluctuation before and after perturbations of model parameters and a Prototypical Distance Ratio (PDR) score to seek the minima by measuring the transferability and discriminability of the models. In this way, the proposed DAS approach can effectively represent the degree of flat minima and evaluate the model generalization ability on the target domain. We have conducted extensive experiments on various DAOD benchmarks and approaches, and the experimental results show that the proposed DAS correlates well with the performance of DAOD models and can be used as an effective tool for model selection after training. The code will be released at https://github.com/HenryYu23/DAS. | Towards Unsupervised Model Selection for Domain Adaptive Object Detection | [
"Hengfu Yu",
"Jinhong Deng",
"Wen Li",
"Lixin Duan"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=gXWmhzeVmh | @inproceedings{
moreno-pino2024rough,
title={Rough Transformers: Lightweight Continuous-Time Sequence Modelling with Path Signatures},
author={Fernando Moreno-Pino and Alvaro Arroyo and Harrison Waldon and Xiaowen Dong and Alvaro Cartea},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gXWmhzeVmh}
} | Time-series data in real-world settings typically exhibit long-range dependencies and are observed at non-uniform intervals. In these settings, traditional sequence-based recurrent models struggle. To overcome this, researchers often replace recurrent models with Neural ODE-based architectures to account for irregularly sampled data and use Transformer-based architectures to account for long-range dependencies. Despite the success of these two approaches, both incur very high computational costs for input sequences of even moderate length. To address this challenge, we introduce the Rough Transformer, a variation of the Transformer model that operates on continuous-time representations of input sequences and incurs significantly lower computational costs. In particular, we propose multi-view signature attention, which uses path signatures to augment vanilla attention and to capture both local and global (multi-scale) dependencies in the input data, while remaining robust to changes in the sequence length and sampling frequency and yielding improved spatial processing. We find that, on a variety of time-series-related tasks, Rough Transformers consistently outperform their vanilla attention counterparts while obtaining the representational benefits of Neural ODE-based models, all at a fraction of the computational time and memory resources. | Rough Transformers: Lightweight Continuous-Time Sequence Modelling with Path Signatures | [
"Fernando Moreno-Pino",
"Alvaro Arroyo",
"Harrison Waldon",
"Xiaowen Dong",
"Alvaro Cartea"
] | NeurIPS.cc/2024/Conference | 2405.20799 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gW0znG5JCG | @inproceedings{
um2024genegene,
title={Gene-Gene Relationship Modeling Based on Genetic Evidence for Single-Cell {RNA}-Seq Data Imputation},
author={Daeho Um and Ji Won Yoon and Seong Jin Ahn and Yunha Yeo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gW0znG5JCG}
} | Single-cell RNA sequencing (scRNA-seq) technologies enable the exploration of cellular heterogeneity and facilitate the construction of cell atlases. However, scRNA-seq data often contain a large portion of missing values (false zeros) or noisy values, hindering downstream analyses. To recover these false zeros, propagation-based imputation methods have been proposed using $k$-NN graphs. However they model only associating relationships among genes within a cell, while, according to well-known genetic evidence, there are both associating and dissociating relationships among genes. To apply this genetic evidence to gene-gene relationship modeling, this paper proposes a novel imputation method that newly employs dissociating relationships in addition to associating relationships. Our method constructs a $k$-NN graph to additionally model dissociating relationships via the negation of a given cell-gene matrix. Moreover, our method standardizes the value distribution (mean and variance) of each gene to have standard distributions regardless of the gene. Through extensive experiments, we demonstrate that the proposed method achieves exceptional performance gains over state-of-the-art methods in both cell clustering and gene expression recovery across six scRNA-seq datasets, validating the significance of using complete gene-gene relationships in accordance with genetic evidence. | Gene-Gene Relationship Modeling Based on Genetic Evidence for Single-Cell RNA-Seq Data Imputation | [
"Daeho Um",
"Ji Won Yoon",
"Seong Jin Ahn",
"Yunha Yeo"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=gVTkMsaaGI | @inproceedings{
venkatraman2024amortizing,
title={Amortizing intractable inference in diffusion models for vision, language, and control},
author={Siddarth Venkatraman and Moksh Jain and Luca Scimeca and Minsu Kim and Marcin Sendera and Mohsin Hasan and Luke Rowe and Sarthak Mittal and Pablo Lemos and Emmanuel Bengio and Alexandre Adam and Jarrid Rector-Brooks and Yoshua Bengio and Glen Berseth and Nikolay Malkin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gVTkMsaaGI}
} | Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors in downstream tasks poses an intractable posterior inference problem. This paper studies *amortized* sampling of the posterior over data, $\mathbf{x}\sim p^{\rm post}(\mathbf{x})\propto p(\mathbf{x})r(\mathbf{x})$, in a model that consists of a diffusion generative model prior $p(\mathbf{x})$ and a black-box constraint or likelihood function $r(\mathbf{x})$. We state and prove the asymptotic correctness of a data-free learning objective, *relative trajectory balance*, for training a diffusion model that samples from this posterior, a problem that existing methods solve only approximately or in restricted cases. Relative trajectory balance arises from the generative flow network perspective on diffusion models, which allows the use of deep reinforcement learning techniques to improve mode coverage. Experiments illustrate the broad potential of unbiased inference of arbitrary posteriors under diffusion priors: in vision (classifier guidance), language (infilling under a discrete diffusion LLM), and multimodal data (text-to-image generation). Beyond generative modeling, we apply relative trajectory balance to the problem of continuous control with a score-based behavior prior, achieving state-of-the-art results on benchmarks in offline reinforcement learning. Code is available at [this link](https://github.com/GFNOrg/diffusion-finetuning). | Amortizing intractable inference in diffusion models for vision, language, and control | [
"Siddarth Venkatraman",
"Moksh Jain",
"Luca Scimeca",
"Minsu Kim",
"Marcin Sendera",
"Mohsin Hasan",
"Luke Rowe",
"Sarthak Mittal",
"Pablo Lemos",
"Emmanuel Bengio",
"Alexandre Adam",
"Jarrid Rector-Brooks",
"Yoshua Bengio",
"Glen Berseth",
"Nikolay Malkin"
] | NeurIPS.cc/2024/Conference | 2405.20971 | [
"https://github.com/gfnorg/diffusion-finetuning"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gVM2AZ5xA6 | @inproceedings{
chu2024generalizable,
title={Generalizable and Animatable Gaussian Head Avatar},
author={Xuangeng Chu and Tatsuya Harada},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gVM2AZ5xA6}
} | In this paper, we propose Generalizable and Animatable Gaussian head Avatar (GAGA) for one-shot animatable head avatar reconstruction.
Existing methods rely on neural radiance fields, leading to heavy rendering consumption and low reenactment speeds.
To address these limitations, we generate the parameters of 3D Gaussians from a single image in a single forward pass.
The key innovation of our work is the proposed dual-lifting method, which produces high-fidelity 3D Gaussians that capture identity and facial details.
Additionally, we leverage global image features and the 3D morphable model to construct 3D Gaussians for controlling expressions.
After training, our model can reconstruct unseen identities without specific optimizations and perform reenactment rendering at real-time speeds.
Experiments show that our method exhibits superior performance compared to previous methods in terms of reconstruction quality and expression accuracy.
We believe our method can establish new benchmarks for future research and advance applications of digital avatars. | Generalizable and Animatable Gaussian Head Avatar | [
"Xuangeng Chu",
"Tatsuya Harada"
] | NeurIPS.cc/2024/Conference | 2410.07971 | [
"https://github.com/xg-chu/gagavatar"
] | https://huggingface.co/papers/2410.07971 | 0 | 0 | 0 | 2 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=gUEBXGV8JM | @inproceedings{
zheng2024aliasfree,
title={Alias-Free Mamba Neural Operator},
author={Jianwei Zheng and LiweiNo and Ni Xu and Junwei Zhu and XiaoxuLin and Xiaoqin Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gUEBXGV8JM}
} | Benefiting from the booming deep learning techniques, neural operators (NO) are considered as an ideal alternative to break the traditions of solving Partial Differential Equations (PDE) with expensive cost.
Yet with the remarkable progress, current solutions concern little on the holistic function features--both global and local information-- during the process of solving PDEs.
Besides, a meticulously designed kernel integration to meet desirable performance often suffers from a severe computational burden, such as GNO with $O(N(N-1))$, FNO with $O(NlogN)$, and Transformer-based NO with $O(N^2)$.
To counteract the dilemma, we propose a mamba neural operator with $O(N)$ computational complexity, namely MambaNO.
Functionally, MambaNO achieves a clever balance between global integration, facilitated by state space model of Mamba that scans the entire function, and local integration, engaged with an alias-free architecture. We prove a property of continuous-discrete equivalence to show the capability of
MambaNO in approximating operators arising from universal PDEs to desired accuracy. MambaNOs are evaluated on a diverse set of benchmarks with possibly multi-scale solutions and set new state-of-the-art scores, yet with fewer parameters and better efficiency. | Alias-Free Mamba Neural Operator | [
"Jianwei Zheng",
"LiweiNo",
"Ni Xu",
"Junwei Zhu",
"XiaoxuLin",
"Xiaoqin Zhang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=gSGLkCX9sc | @inproceedings{
ma2024automated,
title={Automated Label Unification for Multi-Dataset Semantic Segmentation with {GNN}s},
author={Rong Ma and Jie Chen and Xiangyang Xue and Jian Pu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gSGLkCX9sc}
} | Deep supervised models possess significant capability to assimilate extensive training data, thereby presenting an opportunity to enhance model performance through training on multiple datasets. However, conflicts arising from different label spaces among datasets may adversely affect model performance. In this paper, we propose a novel approach to automatically construct a unified label space across multiple datasets using graph neural networks. This enables semantic segmentation models to be trained simultaneously on multiple datasets, resulting in performance improvements. Unlike existing methods, our approach facilitates seamless training without the need for additional manual reannotation or taxonomy reconciliation. This significantly enhances the efficiency and effectiveness of multi-dataset segmentation model training. The results demonstrate that our method significantly outperforms other multi-dataset training methods when trained on seven datasets simultaneously, and achieves state-of-the-art performance on the WildDash 2 benchmark. Our code can be found in https://github.com/Mrhonor/AutoUniSeg. | Automated Label Unification for Multi-Dataset Semantic Segmentation with GNNs | [
"Rong Ma",
"Jie Chen",
"Xiangyang Xue",
"Jian Pu"
] | NeurIPS.cc/2024/Conference | 2407.10534 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gRG6SzbW9p | @inproceedings{
poddar2024personalizing,
title={Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning},
author={Sriyash Poddar and Yanming Wan and Hamish Ivison and Abhishek Gupta and Natasha Jaques},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gRG6SzbW9p}
} | Reinforcement Learning from Human Feedback (RLHF) is a powerful paradigm for aligning foundation models to human values and preferences. However, current RLHF techniques cannot account for the naturally occurring differences in individual human preferences across a diverse population. When these differences arise, traditional RLHF frameworks simply average over them, leading to inaccurate rewards and poor performance for individual subgroups. To address the need for pluralistic alignment, we develop a class of multimodal RLHF methods. Our proposed techniques are based on a latent variable formulation - inferring a novel user-specific latent and learning reward models and policies conditioned on this latent without additional user-specific data. While conceptually simple, we show that in practice, this reward modeling requires careful algorithmic considerations around model architecture and reward scaling. To empirically validate our proposed technique, we first show that it can provide a way to combat underspecification in simulated control problems, inferring and optimizing user-specific reward functions. Next, we conduct experiments on pluralistic language datasets representing diverse user preferences and demonstrate improved reward function accuracy. We additionally show the benefits of this probabilistic framework in terms of measuring uncertainty, and actively learning user preferences. This work enables learning from diverse populations of users with divergent preferences, an important challenge that naturally occurs in problems from robot learning to foundation model alignment. | Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning | [
"Sriyash Poddar",
"Yanming Wan",
"Hamish Ivison",
"Abhishek Gupta",
"Natasha Jaques"
] | NeurIPS.cc/2024/Conference | 2408.10075 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=gPtiGRaVcE | @inproceedings{
zhou2024road,
title={Road Network Representation Learning with the Third Law of Geography},
author={Haicang Zhou and Weiming Huang and Yile Chen and Tiantian He and Gao Cong and Yew-Soon Ong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gPtiGRaVcE}
} | Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks. In this paper, we identify the limitations of existing methods, particularly their overemphasis on the distance effect as outlined in the First Law of Geography. In response, we propose to endow road network representation with the principles of the recent Third Law of Geography. To this end, we propose a novel graph contrastive learning framework that employs geographic configuration-aware graph augmentation and spectral negative sampling, ensuring that road segments with similar geographic configurations yield similar representations, and vice versa, aligning with the principles stated in the Third Law. The framework further fuses the Third Law with the First Law through a dual contrastive learning objective to effectively balance the implications of both laws. We evaluate our framework on two real-world datasets across three downstream tasks. The results show that the integration of the Third Law significantly improves the performance of road segment representations in downstream tasks. | Road Network Representation Learning with the Third Law of Geography | [
"Haicang Zhou",
"Weiming Huang",
"Yile Chen",
"Tiantian He",
"Gao Cong",
"Yew-Soon Ong"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=gPhBvrPdEs | @inproceedings{
bracha2024wormhole,
title={Wormhole Loss for Partial Shape Matching},
author={Amit Bracha and Thomas Dag{\`e}s and Ron Kimmel},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gPhBvrPdEs}
} | When matching parts of a surface to its whole, a fundamental question arises: Which points should be included in the matching process? The issue is intensified when using isometry to measure similarity, as it requires the validation of whether distances measured between pairs of surface points should influence the matching process. The approach we propose treats surfaces as manifolds equipped with geodesic distances, and addresses the partial shape matching challenge by introducing a novel criterion to meticulously search for consistent distances between pairs of points. The new criterion explores the relation between intrinsic geodesic distances between the points, geodesic distances between the points and surface boundaries, and extrinsic distances between boundary points measured in the embedding space. It is shown to be less restrictive compared to previous measures and achieves state-of-the-art results when used as a loss function in training networks for partial shape matching. | Wormhole Loss for Partial Shape Matching | [
"Amit Bracha",
"Thomas Dagès",
"Ron Kimmel"
] | NeurIPS.cc/2024/Conference | 2410.22899 | [
"https://github.com/ABracha/Wormhole"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gPCesxD4B4 | @inproceedings{
sarkar2024gomaageo,
title={{GOMAA}-Geo: {GO}al Modality Agnostic Active Geo-localization},
author={Anindya Sarkar and Srikumar Sastry and Aleksis Pirinen and Chongjie Zhang and Nathan Jacobs and Yevgeniy Vorobeychik},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gPCesxD4B4}
} | We consider the task of active geo-localization (AGL) in which an agent uses a sequence of visual cues observed during aerial navigation to find a target specified through multiple possible modalities. This could emulate a UAV involved in a search-and-rescue operation navigating through an area, observing a stream of aerial images as it goes. The AGL task is associated with two important challenges. Firstly, an agent must deal with a goal specification in one of multiple modalities (e.g., through a natural language description) while the search cues are provided in other modalities (aerial imagery). The second challenge is limited localization time (e.g., limited battery life, urgency) so that the goal must be localized as efficiently as possible, i.e. the agent must effectively leverage its sequentially observed aerial views when searching for the goal. To address these challenges, we propose GOMAA-Geo -- a goal modality agnostic active geo-localization agent -- for zero-shot generalization between different goal modalities. Our approach combines cross-modality contrastive learning to align representations across modalities with supervised foundation model pretraining and reinforcement learning to obtain highly effective navigation and localization policies. Through extensive evaluations, we show that GOMAA-Geo outperforms alternative learnable approaches and that it generalizes across datasets -- e.g., to disaster-hit areas without seeing a single disaster scenario during training -- and goal modalities -- e.g., to ground-level imagery or textual descriptions, despite only being trained with goals specified as aerial views. Our code is available at: https://github.com/mvrl/GOMAA-Geo. | GOMAA-Geo: GOal Modality Agnostic Active Geo-localization | [
"Anindya Sarkar",
"Srikumar Sastry",
"Aleksis Pirinen",
"Chongjie Zhang",
"Nathan Jacobs",
"Yevgeniy Vorobeychik"
] | NeurIPS.cc/2024/Conference | 2406.01917 | [
"https://github.com/mvrl/gomaa-geo"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gOtt78AQk4 | @inproceedings{
qian2024adaptive,
title={Adaptive Domain Learning for Cross-domain Image Denoising},
author={Zian Qian and Chenyang Qi and Ka Lung Law and Hao Fu and Chenyang Lei and Qifeng Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gOtt78AQk4}
} | Different camera sensors have different noise patterns, and thus an image denoising model trained on one sensor often does not generalize well to a different sensor. One plausible solution is to collect a large dataset for each sensor for training or fine-tuning, which is inevitably time-consuming. To address this cross-domain challenge, we present a novel adaptive domain learning (ADL) scheme for cross-domain RAW image denoising by utilizing existing data from different sensors (source domain) plus a small amount of data from the new sensor (target domain). The ADL training scheme automatically removes the data in the source domain that are harmful to fine-tuning a model for the target domain (some data are harmful as adding them during training lowers the performance due to domain gaps). Also, we introduce a modulation module to adopt sensor-specific information (sensor type and ISO) to understand input data for image denoising. We conduct extensive experiments on public datasets with various smartphone and DSLR cameras, which show our proposed model outperforms prior work on cross-domain image denoising, given a small amount of image data from the target domain sensor. | Adaptive Domain Learning for Cross-domain Image Denoising | [
"Zian Qian",
"Chenyang Qi",
"Ka Lung Law",
"Hao Fu",
"Chenyang Lei",
"Qifeng Chen"
] | NeurIPS.cc/2024/Conference | 2411.01472 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gN1iKwxlL5 | @inproceedings{
tanneau2024dual,
title={Dual Lagrangian Learning for Conic Optimization},
author={Mathieu Tanneau and Pascal Van Hentenryck},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gN1iKwxlL5}
} | This paper presents Dual Lagrangian Learning (DLL), a principled learning methodology for dual conic optimization proxies.
DLL leverages conic duality and the representation power of ML models to provide high-duality, dual-feasible solutions, and therefore valid Lagrangian dual bounds, for linear and nonlinear conic optimization problems.
The paper introduces a systematic dual completion procedure, differentiable conic projection layers, and a self-supervised learning framework based on Lagrangian duality.
It also provides closed-form dual completion formulae for broad classes of conic problems, which eliminate the need for costly implicit layers.
The effectiveness of DLL is demonstrated on linear and nonlinear conic optimization problems.
The proposed methodology significantly outperforms a state-of-the-art learning-based method, and achieves 1000x speedups over commercial interior-point solvers with optimality gaps under 0.5\% on average. | Dual Lagrangian Learning for Conic Optimization | [
"Mathieu Tanneau",
"Pascal Van Hentenryck"
] | NeurIPS.cc/2024/Conference | 2402.03086 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gMqaKJCOCB | @inproceedings{
pareek2024understanding,
title={Understanding the Gains from Repeated Self-Distillation},
author={Divyansh Pareek and Simon Shaolei Du and Sewoong Oh},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gMqaKJCOCB}
} | Self-Distillation is a special type of knowledge distillation where the student model has the same architecture as the teacher model. Despite using the same architecture and the same training data, self-distillation has been empirically observed to improve performance, especially when applied repeatedly. For such a process, there is a fundamental question of interest: How much gain is possible by applying multiple steps of self-distillation? To investigate this relative gain, we propose using the simple but canonical task of linear regression. Our analysis shows that the excess risk achieved by multi-step self-distillation can significantly improve upon a single step of self-distillation, reducing the excess risk by a factor of $d$, where $d$ is the input dimension. Empirical results on regression tasks from the UCI repository show a reduction in the learnt model's risk (MSE) by up to $47$%. | Understanding the Gains from Repeated Self-Distillation | [
"Divyansh Pareek",
"Simon Shaolei Du",
"Sewoong Oh"
] | NeurIPS.cc/2024/Conference | 2407.04600 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gLoe70Tn8V | @inproceedings{
ha2024finegrained,
title={Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random},
author={Mingming Ha and Taoxuewen and Wenfang Lin and QIONGXU MA and Wujiang Xu and Linxun Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gLoe70Tn8V}
} | In most practical applications such as recommendation systems, display advertising, and so forth, the collected data often contains missing values and those missing values are generally missing-not-at-random, which deteriorates the prediction performance of models. Some existing estimators and regularizers attempt to achieve unbiased estimation to improve the predictive performance. However, variances and generalization bound of these methods are generally unbounded when the propensity scores tend to zero, compromising their stability and robustness. In this paper, we first theoretically reveal that limitations of regularization techniques. Besides, we further illustrate that, for more general estimators, unbiasedness will inevitably lead to unbounded variance. These general laws inspire us that the estimator designs is not merely about eliminating bias, reducing variance, or simply achieve a bias-variance trade-off. Instead, it involves a quantitative joint optimization of bias and variance. Then, we develop a systematic fine-grained dynamic learning framework to jointly optimize bias and variance, which adaptively selects an appropriate estimator for each user-item pair according to the predefined objective function. With this operation, the generalization bounds and variances of models are reduced and bounded with theoretical guarantees. Extensive experiments are conducted to verify the theoretical results and the effectiveness of the proposed dynamic learning framework. | Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random | [
"Mingming Ha",
"Taoxuewen",
"Wenfang Lin",
"QIONGXU MA",
"Wujiang Xu",
"Linxun Chen"
] | NeurIPS.cc/2024/Conference | 2405.15403 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gL5nT4y8fn | @inproceedings{
zhong2024panacea,
title={Panacea: Pareto Alignment via Preference Adaptation for {LLM}s},
author={Yifan Zhong and Chengdong Ma and Xiaoyuan Zhang and Ziran Yang and Haojun Chen and Qingfu Zhang and Siyuan Qi and Yaodong Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gL5nT4y8fn}
} | Current methods for large language model alignment typically use scalar human preference labels. However, this convention tends to oversimplify the multi-dimensional and heterogeneous nature of human preferences, leading to reduced expressivity and even misalignment. This paper presents Panacea, an innovative approach that reframes alignment as a multi-dimensional preference optimization problem. Panacea trains a single model capable of adapting online and Pareto-optimally to diverse sets of preferences without the need for further tuning. A major challenge here is using a low-dimensional preference vector to guide the model's behavior, despite it being governed by an overwhelmingly large number of parameters. To address this, Panacea is designed to use singular value decomposition (SVD)-based low-rank adaptation, which allows the preference vector to be simply injected online as singular values. Theoretically, we prove that Panacea recovers the entire Pareto front with common loss aggregation methods under mild conditions. Moreover, our experiments demonstrate, for the first time, the feasibility of aligning a single LLM to represent an exponentially vast spectrum of human preferences through various optimization methods. Our work marks a step forward in effectively and efficiently aligning models to diverse and intricate human preferences in a controllable and Pareto-optimal manner. | Panacea: Pareto Alignment via Preference Adaptation for LLMs | [
"Yifan Zhong",
"Chengdong Ma",
"Xiaoyuan Zhang",
"Ziran Yang",
"Haojun Chen",
"Qingfu Zhang",
"Siyuan Qi",
"Yaodong Yang"
] | NeurIPS.cc/2024/Conference | 2402.02030 | [
""
] | https://huggingface.co/papers/2402.02030 | 2 | 10 | 0 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=gKMTM1i8Ew | @inproceedings{
poiani2024optimal,
title={Optimal Multi-Fidelity Best-Arm Identification},
author={Riccardo Poiani and R{\'e}my Degenne and Emilie Kaufmann and Alberto Maria Metelli and Marcello Restelli},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gKMTM1i8Ew}
} | In bandit best-arm identification, an algorithm is tasked with finding the arm with highest mean reward with a specified accuracy as fast as possible. We study multi-fidelity best-arm identification, in which the algorithm can choose to sample an arm at a lower fidelity (less accurate mean estimate) for a lower cost. Several methods have been proposed for tackling this problem, but their optimality remain elusive, notably due to loose lower bounds on the total cost needed to identify the best arm. Our first contribution is a tight, instance-dependent lower bound on the cost complexity. The study of the optimization problem featured in the lower bound provides new insights to devise computationally efficient algorithms, and leads us to propose a gradient-based approach with asymptotically optimal cost complexity. We demonstrate the benefits of the new algorithm compared to existing methods in experiments. Our theoretical and empirical findings also shed light on an intriguing concept of optimal fidelity for each arm. | Optimal Multi-Fidelity Best-Arm Identification | [
"Riccardo Poiani",
"Rémy Degenne",
"Emilie Kaufmann",
"Alberto Maria Metelli",
"Marcello Restelli"
] | NeurIPS.cc/2024/Conference | 2406.03033 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gKLgY3m9zj | @inproceedings{
correia2024an,
title={An Information Theoretic Perspective on Conformal Prediction},
author={Alvaro Correia and Fabio Valerio Massoli and Christos Louizos and Arash Behboodi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gKLgY3m9zj}
} | Conformal Prediction (CP) is a distribution-free uncertainty estimation framework that constructs prediction sets guaranteed to contain the true answer with a user-specified probability. Intuitively, the size of the prediction set encodes a general notion of uncertainty, with larger sets associated with higher degrees of uncertainty. In this work, we leverage information theory to connect conformal prediction to other notions of uncertainty. More precisely, we prove three different ways to upper bound the intrinsic uncertainty, as described by the conditional entropy of the target variable given the inputs, by combining CP with information theoretical inequalities. Moreover, we demonstrate two direct and useful applications of such connection between conformal prediction and information theory: (i) more principled and effective conformal training objectives that generalize previous approaches and enable end-to-end training of machine learning models from scratch, and (ii) a natural mechanism to incorporate side information into conformal prediction. We empirically validate both applications in centralized and federated learning settings, showing our theoretical results translate to lower inefficiency (average prediction set size) for popular CP methods. | An Information Theoretic Perspective on Conformal Prediction | [
"Alvaro Correia",
"Fabio Valerio Massoli",
"Christos Louizos",
"Arash Behboodi"
] | NeurIPS.cc/2024/Conference | 2405.02140 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gJxEiRcnao | @inproceedings{
abel2024biologicallyinspired,
title={Biologically-Inspired Learning Model for Instructed Vision},
author={Roy Abel and Shimon Ullman},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gJxEiRcnao}
} | As part of the effort to understand how the brain learns, ongoing research seeks to combine biological knowledge with current artificial intelligence (AI) modeling in an attempt to find an efficient biologically plausible learning scheme. Current models often use a cortical-like combination of bottom-up (BU) and top-down (TD) processing, where the TD part carries feedback signals for learning. However, in the visual cortex, the TD pathway plays a second major role in visual attention, by guiding the visual process toward locations and tasks of interest. A biological model should therefore integrate both learning and visual guidance. We introduce a model that uses a cortical-like combination of BU and TD processing that naturally integrates the two major functions of the TD stream. This integration is achieved through an appropriate connectivity pattern between the BU and TD streams, a novel processing cycle that uses the TD stream twice, and a 'Counter-Hebb' learning mechanism that operates across both streams. We show that the 'Counter-Hebb' mechanism can provide an exact backpropagation synaptic modification. Additionally, our model can effectively guide the visual stream to perform a task of interest, achieving competitive performance on standard multi-task learning benchmarks compared to AI models. The successful combination of learning and visual guidance could provide a new view on combining BU and TD processing in human vision and suggests possible directions for both biologically plausible models and artificial instructed models, such as vision-language models (VLMs). | Biologically-Inspired Learning Model for Instructed Vision | [
"Roy Abel",
"Shimon Ullman"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=gJbZyKGfd6 | @inproceedings{
yang2024hyperlogic,
title={HyperLogic: Enhancing Diversity and Accuracy in Rule Learning with HyperNets},
author={Yang Yang and Wendi Ren and Shuang Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gJbZyKGfd6}
} | Exploring the integration of if-then logic rules within neural network architectures presents an intriguing area. This integration seamlessly transforms the rule learning task into neural network training using backpropagation and stochastic gradient descent. From a well-trained sparse and shallow neural network, one can interpret each layer and neuron through the language of logic rules, and a global explanatory rule set can be directly extracted. However, ensuring interpretability may impose constraints on the flexibility, depth, and width of neural networks. In this paper, we propose HyperLogic: a novel framework leveraging hypernetworks to generate weights of the main network. HyperLogic can unveil multiple diverse rule sets, each capable of capturing heterogeneous patterns in data. This provides a simple yet effective method to increase model flexibility and preserve interpretability. We theoretically analyzed the benefits of the HyperLogic by examining the approximation error and generalization capabilities under two types of regularization terms: sparsity and diversity regularizations. Experiments on real data demonstrate that our method can learn more diverse, accurate, and concise rules. | HyperLogic: Enhancing Diversity and Accuracy in Rule Learning with HyperNets | [
"Yang Yang",
"Wendi Ren",
"Shuang Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=gITGmIEinf | @inproceedings{
kacham2024approximating,
title={Approximating the Top Eigenvector in Random Order Streams},
author={Praneeth Kacham and David Woodruff},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gITGmIEinf}
} | When rows of an $n \times d$ matrix $A$ are given in a stream, we study algorithms for approximating the top eigenvector of $A^T A$ (equivalently, the top right singular vector of $A$). We consider worst case inputs $A$ but assume that the rows are presented to the streaming algorithm in a uniformly random order. We show that when the gap parameter $R = \sigma_1(A)^2/\sigma_2(A)^2 = \Omega(1)$, then there is a randomized algorithm that uses $O(h \cdot d \cdot \text{polylog}(d))$ bits of space and outputs a unit vector $v$ that has a correlation $1 - O(1/\sqrt{R})$ with the top eigenvector $v_1$. Here $h$ denotes the number of ``heavy rows'' in the matrix, defined as the rows with Euclidean norm at least $\|{A}\|_F/\sqrt{d \cdot \text{polylog}(d)}$. We also provide a lower bound showing that any algorithm using $O(hd/R)$ bits of space can obtain at most $1 - \Omega(1/R^2)$ correlation with the top eigenvector. Thus, parameterizing the space complexity in terms of the number of heavy rows is necessary for high accuracy solutions.
Our results improve upon the $R = \Omega(\log n \cdot \log d)$ requirement in a recent work of Price. We note that Price's algorithm works for arbitrary order streams whereas our algorithm requires a stronger assumption that the rows are presented in a uniformly random order. We additionally show that the gap requirements in Price's analysis can be brought down to $R = \Omega(\log^2 d)$ for arbitrary order streams and $R = \Omega(\log d)$ for random order streams. The requirement of $R = \Omega(\log d)$ for random order streams is nearly tight for Price's analysis as we obtain a simple instance with $R = \Omega(\log d/\log\log d)$ for which Price's algorithm, with any fixed learning rate, cannot output a vector approximating the top eigenvector $v_1$. | Approximating the Top Eigenvector in Random Order Streams | [
"Praneeth Kacham",
"David Woodruff"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=gHYhVSCtDH | @inproceedings{
zhang2024voxel,
title={Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection},
author={Guowen Zhang and Lue Fan and Chenhang HE and Zhen Lei and Zhaoxiang Zhang and Lei Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gHYhVSCtDH}
} | Serialization-based methods, which serialize the 3D voxels and group them into multiple sequences before inputting to Transformers, have demonstrated their effectiveness in 3D object detection. However, serializing 3D voxels into 1D sequences will inevitably sacrifice the voxel spatial proximity. Such an issue is hard to be addressed by enlarging the group size with existing serialization-based methods due to the quadratic complexity of Transformers with feature sizes. Inspired by the recent advances of state space models (SSMs), we present a Voxel SSM, termed as Voxel Mamba, which employs a group-free strategy to serialize the whole space of voxels into a single sequence. The linear complexity of SSMs encourages our group-free design, alleviating the loss of spatial proximity of voxels. To further enhance the spatial proximity, we propose a Dual-scale SSM Block to establish a hierarchical structure, enabling a larger receptive field in the 1D serialization curve, as well as more complete local regions in 3D space. Moreover, we implicitly apply window partition under the group-free framework by positional encoding, which further enhances spatial proximity by encoding voxel positional information. Our experiments on Waymo Open Dataset and nuScenes dataset show that Voxel Mamba not only achieves higher accuracy than state-of-the-art methods, but also demonstrates significant advantages in computational efficiency. The source code is available at https://github.com/gwenzhang/Voxel-Mamba. | Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection | [
"Guowen Zhang",
"Lue Fan",
"Chenhang HE",
"Zhen Lei",
"Zhaoxiang Zhang",
"Lei Zhang"
] | NeurIPS.cc/2024/Conference | 2406.10700 | [
"https://github.com/gwenzhang/voxel-mamba"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=gHCFduRo7o | @inproceedings{
paes2024selective,
title={Selective Explanations},
author={Lucas Monteiro Paes and Dennis Wei and Flavio Calmon},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gHCFduRo7o}
} | Feature attribution methods explain black-box machine learning (ML) models by assigning importance scores to input features.
These methods can be computationally expensive for large ML models. To address this challenge, there have been increasing efforts to develop amortized explainers, where a ML model is trained to efficiently approximate computationally expensive feature attribution scores. Despite their efficiency, amortized explainers can produce misleading explanations. In this paper, we propose selective explanations to (i) detect when amortized explainers generate inaccurate explanations and (ii) improve the approximation of the explanation using a technique we call explanations with initial guess. Selective explanations allow practitioners to specify the fraction of samples that receive explanations with initial guess, offering a principled way to bridge the gap between amortized explainers (one inference) and more computationally costly approximations (multiple inferences). Our experiments on various models and datasets demonstrate that feature attributions via selective explanations strike a favorable balance between explanation quality and computational efficiency. | Selective Explanations | [
"Lucas Monteiro Paes",
"Dennis Wei",
"Flavio Calmon"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=gGR9dJbe3r | @inproceedings{
gilboa2024exponential,
title={Exponential Quantum Communication Advantage in Distributed Inference and Learning},
author={Dar Gilboa and Hagay Michaeli and Daniel Soudry and Jarrod Ryan McClean},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gGR9dJbe3r}
} | Training and inference with large machine learning models that far exceed the memory capacity of individual devices necessitates the design of distributed architectures, forcing one to contend with communication constraints. We present a framework for distributed computation over a quantum network in which data is encoded into specialized quantum states. We prove that for models within this framework, inference and training using gradient descent can be performed with exponentially less communication compared to their classical analogs, and with relatively modest overhead relative to standard gradient-based methods. We show that certain graph neural networks are particularly amenable to implementation within this framework, and moreover present empirical evidence that they perform well on standard benchmarks.
To our knowledge, this is the first example of exponential quantum advantage for a generic class of machine learning problems that hold regardless of the data encoding cost.
Moreover, we show that models in this class can encode highly nonlinear features of their inputs, and their expressivity increases exponentially with model depth.
We also delineate the space of models for which exponential communication advantages hold by showing that they cannot hold for linear classification.
Communication of quantum states that potentially limit the amount of information that can be extracted from them about the data and model parameters may also lead to improved privacy guarantees for distributed computation. Taken as a whole, these findings form a promising foundation for distributed machine learning over quantum networks. | Exponential Quantum Communication Advantage in Distributed Inference and Learning | [
"Dar Gilboa",
"Hagay Michaeli",
"Daniel Soudry",
"Jarrod Ryan McClean"
] | NeurIPS.cc/2024/Conference | 2310.07136 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gDpWYpocE1 | @inproceedings{
liu2024pandoras,
title={Pandora's Box: Towards Building Universal Attackers against Real-World Large Vision-Language Models},
author={Daizong Liu and Mingyu Yang and Xiaoye Qu and Pan Zhou and Xiang Fang and Keke Tang and Yao Wan and Lichao Sun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gDpWYpocE1}
} | Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a wide range of multimodal understanding tasks. Nevertheless, these models are susceptible to adversarial examples. In real-world applications, existing LVLM attackers generally rely on the detailed prior knowledge of the model to generate effective perturbations. Moreover, these attacks are task-specific, leading to significant costs for designing perturbation. Motivated by the research gap and practical demands, in this paper, we make the first attempt to build a universal attacker against real-world LVLMs, focusing on two critical aspects: (i) restricting access to only the LVLM inputs and outputs. (ii) devising a universal adversarial patch, which is task-agnostic and can deceive any LVLM-driven task when applied to various inputs. Specifically, we start by initializing the location and the pattern of the adversarial patch through random sampling, guided by the semantic distance between their output and the target label. Subsequently, we maintain a consistent patch location while refining the pattern to enhance semantic resemblance to the target. In particular, our approach incorporates a diverse set of LVLM task inputs as query samples to approximate the patch gradient, capitalizing on the importance of distinct inputs. In this way, the optimized patch is universally adversarial against different tasks and prompts, leveraging solely gradient estimates queried from the model. Extensive experiments are conducted to verify the strong universal adversarial capabilities of our proposed attack with prevalent LVLMs including LLaVA, MiniGPT-4, Flamingo, and BLIP-2, spanning a spectrum of tasks, all achieved without delving into the details of the model structures. | Pandora's Box: Towards Building Universal Attackers against Real-World Large Vision-Language Models | [
"Daizong Liu",
"Mingyu Yang",
"Xiaoye Qu",
"Pan Zhou",
"Xiang Fang",
"Keke Tang",
"Yao Wan",
"Lichao Sun"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=gCCMzedgbo | @inproceedings{
petersen2024tract,
title={TrAct: Making First-layer Pre-Activations Trainable},
author={Felix Petersen and Christian Borgelt and Stefano Ermon},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gCCMzedgbo}
} | We consider the training of the first layer of vision models and notice the clear relationship between pixel values and gradient update magnitudes: the gradients arriving at the weights of a first layer are by definition directly proportional to (normalized) input pixel values. Thus, an image with low contrast has a smaller impact on learning than an image with higher contrast, and a very bright or very dark image has a stronger impact on the weights than an image with moderate brightness. In this work, we propose performing gradient descent on the embeddings produced by the first layer of the model. However, switching to discrete inputs with an embedding layer is not a reasonable option for vision models. Thus, we propose the conceptual procedure of (i) a gradient descent step on first layer activations to construct an activation proposal, and (ii) finding the optimal weights of the first layer, i.e., those weights which minimize the squared distance to the activation proposal. We provide a closed form solution of the procedure and adjust it for robust stochastic training while computing everything efficiently. Empirically, we find that TrAct (Training Activations) speeds up training by factors between 1.25x and 4x while requiring only a small computational overhead. We demonstrate the utility of TrAct with different optimizers for a range of different vision models including convolutional and transformer architectures. | TrAct: Making First-layer Pre-Activations Trainable | [
"Felix Petersen",
"Christian Borgelt",
"Stefano Ermon"
] | NeurIPS.cc/2024/Conference | 2410.23970 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gC3BzNwqQp | @inproceedings{
bekci2024online,
title={Online Learning of Delayed Choices},
author={Recep Yusuf Bekci},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gC3BzNwqQp}
} | Choice models are essential for understanding decision-making processes in domains like online advertising, product recommendations, and assortment optimization. The Multinomial Logit (MNL) model is particularly versatile in selecting products or advertisements for display. However, challenges arise with unknown MNL parameters and delayed feedback, requiring sellers to learn customers’ choice behavior and make dynamic decisions with biased knowledge due to delays. We address these challenges by developing an algorithm that handles delayed feedback, balancing exploration and exploitation using confidence bounds and optimism. We first consider a censored setting where a threshold for considering feedback is imposed by business requirements. Our algorithm demonstrates a $\tilde{O}(\sqrt{NT})$ regret, with a matching lower bound up to a logarithmic term. Furthermore, we extend our analysis to environments with non-thresholded delays, achieving a $\tilde{O}(\sqrt{NT})$ regret. To validate our approach, we conduct experiments that confirm the effectiveness of our algorithm. | Online Learning of Delayed Choices | [
"Recep Yusuf Bekci"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=gAgwqHOBIg | @inproceedings{
nguyen2024dintr,
title={{DINTR}: Tracking via Diffusion-based Interpolation},
author={Pha Nguyen and Ngan Hoang Le and Jackson Cothren and Alper Yilmaz and Khoa Luu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gAgwqHOBIg}
} | Object tracking is a fundamental task in computer vision, requiring the localization of objects of interest across video frames. Diffusion models have shown remarkable capabilities in visual generation, making them well-suited for addressing several requirements of the tracking problem. This work proposes a novel diffusion-based methodology to formulate the tracking task. Firstly, their conditional process allows for injecting indications of the target object into the generation process. Secondly, diffusion mechanics can be developed to inherently model temporal correspondences, enabling the reconstruction of actual frames in video. However, existing diffusion models rely on extensive and unnecessary mapping to a Gaussian noise domain, which can be replaced by a more efficient and stable interpolation process. Our proposed interpolation mechanism draws inspiration from classic image-processing techniques, offering a more interpretable, stable, and faster approach tailored specifically for the object tracking task. By leveraging the strengths of diffusion models while circumventing their limitations, our Diffusion-based INterpolation TrackeR (DINTR) presents a promising new paradigm and achieves a superior multiplicity on seven benchmarks across five indicator representations. | DINTR: Tracking via Diffusion-based Interpolation | [
"Pha Nguyen",
"Ngan Hoang Le",
"Jackson Cothren",
"Alper Yilmaz",
"Khoa Luu"
] | NeurIPS.cc/2024/Conference | 2410.10053 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=g92nu7knRq | @inproceedings{
chen2024dha,
title={{DHA}: Learning Decoupled-Head Attention from Transformer Checkpoints via Adaptive Heads Fusion},
author={Yilong Chen and Linhao Zhang and Junyuan Shang and Zhenyu Zhang and Tingwen Liu and Shuohuan Wang and Yu Sun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=g92nu7knRq}
} | Large language models (LLMs) with billions of parameters demonstrate impressive performance. However, the widely used Multi-Head Attention (MHA) in LLMs incurs substantial computational and memory costs during inference. While some efforts have optimized attention mechanisms by pruning heads or sharing parameters among heads, these methods often lead to performance degradation or necessitate substantial continued pre-training costs to restore performance. Based on the analysis of attention redundancy, we design a Decoupled-Head Attention (DHA) mechanism. DHA adaptively configures group sharing for key heads and value heads across various layers, achieving a better balance between performance and efficiency. Inspired by the observation of clustering similar heads, we propose to progressively transform the MHA checkpoint into the DHA model through linear fusion of similar head parameters step by step, retaining the parametric knowledge of the MHA checkpoint. We construct DHA models by transforming various scales of MHA checkpoints given target head budgets. Our experiments show that DHA remarkably requires a mere 0.25\% of the original model's pre-training budgets to achieve 97.6\% of performance while saving 75\% of KV cache. Compared to Group-Query Attention (GQA), DHA achieves a 12$\times$ training acceleration, a maximum of 24.85\% performance improvement under 0.2B tokens budget, and finally 2.3\% overall performance improvement. | DHA: Learning Decoupled-Head Attention from Transformer Checkpoints via Adaptive Heads Fusion | [
"Yilong Chen",
"Linhao Zhang",
"Junyuan Shang",
"Zhenyu Zhang",
"Tingwen Liu",
"Shuohuan Wang",
"Yu Sun"
] | NeurIPS.cc/2024/Conference | 2406.06567 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=g8wnC1E1OS | @inproceedings{
huang2024parameter,
title={Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning},
author={Wenke Huang and Mang Ye and Zekun Shi and Guancheng Wan and He Li and Bo Du},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=g8wnC1E1OS}
} | Backdoor attacks pose a serious threat to federated systems, where malicious clients optimize on the triggered distribution to mislead the global model towards a predefined target. Existing backdoor defense methods typically require either homogeneous assumption, validation datasets, or client optimization conflicts. In our work, we observe that benign heterogeneous distributions and malicious triggered distributions exhibit distinct parameter importance degrees. We introduce the Fisher Discrepancy Cluster and Rescale (FDCR) method, which utilizes Fisher Information to calculate the degree of parameter importance for local distributions. This allows us to reweight client parameter updates and identify those with large discrepancies as backdoor attackers. Furthermore, we prioritize rescaling important parameters to expedite adaptation to the target distribution, encouraging significant elements to contribute more while diminishing the influence of trivial ones. This approach enables FDCR to handle backdoor attacks in heterogeneous federated learning environments. Empirical results on various heterogeneous federated scenarios under backdoor attacks demonstrate the effectiveness of our method. | Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning | [
"Wenke Huang",
"Mang Ye",
"Zekun Shi",
"Guancheng Wan",
"He Li",
"Bo Du"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=g8pyTkxyIV | @inproceedings{
lee2024fully,
title={Fully Explicit Dynamic Gaussian Splatting},
author={Junoh Lee and Changyeon Won and Hyunjun Jung and Inhwan Bae and Hae-Gon Jeon},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=g8pyTkxyIV}
} | 3D Gaussian Splatting has shown fast and high-quality rendering results in static scenes by leveraging dense 3D prior and explicit representations. Unfortunately, the benefits of the prior and representation do not involve novel view synthesis for dynamic motions. Ironically, this is because the main barrier is the reliance on them, which requires increasing training and rendering times to account for dynamic motions.
In this paper, we design Explicit 4D Gaussian Splatting (Ex4DGS).
Our key idea is to firstly separate static and dynamic Gaussians during training, and to explicitly sample positions and rotations of the dynamic Gaussians at sparse timestamps. The sampled positions and rotations are then interpolated to represent both spatially and temporally continuous motions of objects in dynamic scenes as well as reducing computational cost.
Additionally, we introduce a progressive training scheme and a point-backtracking technique that improves Ex4DGS's convergence. We initially train Ex4DGS using short timestamps and progressively extend timestamps, which makes it work well with a few point clouds. The point-backtracking is used to quantify the cumulative error of each Gaussian over time, enabling the detection and removal of erroneous Gaussians in dynamic scenes. Comprehensive experiments on various scenes demonstrate the state-of-the-art rendering quality from our method, achieving fast rendering of 62 fps on a single 2080Ti GPU. | Fully Explicit Dynamic Gaussian Splatting | [
"Junoh Lee",
"Changyeon Won",
"Hyunjun Jung",
"Inhwan Bae",
"Hae-Gon Jeon"
] | NeurIPS.cc/2024/Conference | 2410.15629 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=g8kFlZDcaX | @inproceedings{
huang2024decisionfocused,
title={Decision-Focused Learning with Directional Gradients},
author={Michael Huang and Vishal Gupta},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=g8kFlZDcaX}
} | We propose a novel family of decision-aware surrogate losses, called Perturbation Gradient (PG) losses, for the predict-then-optimize framework. These losses directly approximate the downstream decision loss and can be optimized using off-the-shelf gradient-based methods. Importantly, unlike existing surrogate losses, the approximation error of our PG losses vanishes as the number of samples grows. This implies that optimizing our surrogate loss yields a best-in-class policy asymptotically, even in misspecified settings. This is the first such result in misspecified settings and we provide numerical evidence confirming our PG losses substantively outperform existing proposals when the underlying model is misspecified and the noise is not centrally symmetric. Insofar as misspecification is commonplace in practice -- especially when we might prefer a simpler, more interpretable model -- PG losses offer a novel, theoretically justified, method for computationally tractable decision-aware learning. | Decision-Focused Learning with Directional Gradients | [
"Michael Huang",
"Vishal Gupta"
] | NeurIPS.cc/2024/Conference | 2402.03256 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=g7lYP11Erv | @inproceedings{
sun2024pointprc,
title={Point-{PRC}: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud Analysis},
author={Hongyu Sun and Qiuhong Ke and Yongcai Wang and Wang Chen and Kang Yang and Deying Li and Jianfei Cai},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=g7lYP11Erv}
} | This paper investigates the 3D domain generalization (3DDG) ability of large 3D models based on prevalent prompt learning. Recent works demonstrate the performances of 3D point cloud recognition can be boosted remarkably by parameter-efficient prompt tuning. However, we observe that the improvement on downstream tasks comes at the expense of a severe drop in 3D domain generalization. To resolve this challenge, we present a comprehensive regulation framework that allows the learnable prompts to actively interact with the well-learned general knowledge in large 3D models to maintain good generalization. Specifically, the proposed framework imposes multiple explicit constraints on the prompt learning trajectory by maximizing the mutual agreement between task-specific predictions and task-agnostic knowledge. We design the regulation framework as a plug-and-play module to embed into existing representative large 3D models. Surprisingly, our method not only realizes consistently increasing generalization ability but also enhances task-specific 3D recognition performances across various 3DDG benchmarks by a clear margin. Considering the lack of study and evaluation on 3DDG, we also create three new benchmarks, namely base-to-new, cross-dataset and few-shot generalization benchmarks, to enrich the field and inspire future research. Code and benchmarks are available at \url{https://github.com/auniquesun/Point-PRC}. | Point-PRC: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud Analysis | [
"Hongyu Sun",
"Qiuhong Ke",
"Yongcai Wang",
"Wang Chen",
"Kang Yang",
"Deying Li",
"Jianfei Cai"
] | NeurIPS.cc/2024/Conference | 2410.20406 | [
"https://github.com/auniquesun/point-prc"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=g6nn2AijDp | @inproceedings{
kim2024code,
title={{CODE}: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models},
author={Junho Kim and Hyunjun Kim and KIM YEONJU and Yong Man Ro},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=g6nn2AijDp}
} | Large Multi-modal Models (LMMs) have recently demonstrated remarkable abilities in visual context understanding and coherent response generation. However, alongside these advancements, the issue of hallucinations has emerged as a significant challenge, producing erroneous responses that are unrelated to the visual contents. In this paper, we introduce a novel contrastive-based decoding method, COuntering DEscription Contrastive Decoding (CODE), which leverages self-generated descriptions as contrasting references during the decoding phase of LMMs to address hallucination issues. CODE utilizes the comprehensive descriptions from model itself as visual counterpart to correct and improve response alignment with actual visual content. By dynamically adjusting the information flow and distribution of next-token predictions in the LMM's vocabulary, CODE enhances the coherence and informativeness of generated responses. Extensive experiments demonstrate that our method significantly reduces hallucinations and improves cross-modal consistency across various benchmarks and cutting-edge LMMs. Our method provides a simple yet effective decoding strategy that can be integrated to existing LMM frameworks without additional training. | CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models | [
"Junho Kim",
"Hyunjun Kim",
"KIM YEONJU",
"Yong Man Ro"
] | NeurIPS.cc/2024/Conference | 2406.01920 | [
""
] | https://huggingface.co/papers/2406.01920 | 2 | 1 | 1 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=g5DyqerUpX | @inproceedings{
zhu2024sparkle,
title={{SPARKLE}: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization},
author={Shuchen Zhu and Boao Kong and Songtao Lu and Xinmeng Huang and Kun Yuan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=g5DyqerUpX}
} | This paper studies decentralized bilevel optimization, in which multiple agents collaborate to solve problems involving nested optimization structures with neighborhood communications. Most existing literature primarily utilizes gradient tracking to mitigate the influence of data heterogeneity, without exploring other well-known heterogeneity-correction techniques such as EXTRA or Exact Diffusion. Additionally, these studies often employ identical decentralized strategies for both upper- and lower-level problems, neglecting to leverage distinct mechanisms across different levels. To address these limitations, this paper proposes SPARKLE, a unified single-loop primal-dual algorithm framework for decentralized bilevel optimization. SPARKLE offers the flexibility to incorporate various heterogeneity-correction strategies into the algorithm. Moreover, SPARKLE allows for different strategies to solve upper- and lower-level problems. We present a unified convergence analysis for SPARKLE, applicable to all its variants, with state-of-the-art convergence rates compared to existing decentralized bilevel algorithms. Our results further reveal that EXTRA and Exact Diffusion are more suitable for decentralized bilevel optimization, and using mixed strategies in bilevel algorithms brings more benefits than relying solely on gradient tracking. | SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization | [
"Shuchen Zhu",
"Boao Kong",
"Songtao Lu",
"Xinmeng Huang",
"Kun Yuan"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=g3MbZOw0qO | @inproceedings{
liu2024to,
title={To Learn or Not to Learn, That is the Question {\textemdash} A Feature-Task Dual Learning Model of Perceptual Learning},
author={Xiao Liu and Muyang Lyu and Cong Yu and Si Wu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=g3MbZOw0qO}
} | Perceptual learning refers to the practices through which participants learn to improve their performance in perceiving sensory stimuli. Two seemingly conflicting phenomena of specificity and transfer have been widely observed in perceptual learning.
Here, we propose a dual-learning model to reconcile these two phenomena. The model consists of two learning processes. One is task-based learning, which is fast and enables the brain to adapt to a task rapidly by using existing feature representations. The other is feature-based learning, which is slow and enables the brain to improve feature representations to match the statistical change of the environment. Associated with different training paradigms, the interactions between these two learning processes induce the rich phenomena of perceptual learning. Specifically, in the training paradigm where the same stimulus condition is presented excessively, feature-based learning is triggered, which incurs specificity, while in the paradigm where the stimulus condition varies during the training, task-based learning dominates to induce the transfer effect. As the number of training sessions under the same stimulus condition increases, a transition from transfer to specificity occurs.
We demonstrate that the dual-learning model can account for both the specificity and transfer phenomena observed in classical psychophysical experiments. We hope that this study gives us insight into understanding how the brain balances the accomplishment of a new task and the consumption of learning effort. | To Learn or Not to Learn, That is the Question — A Feature-Task Dual Learning Model of Perceptual Learning | [
"Xiao Liu",
"Muyang Lyu",
"Cong Yu",
"Si Wu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=g1HxCIc0wi | @inproceedings{
cheng2024speculative,
title={Speculative Monte-Carlo Tree Search},
author={Scott Cheng and Mahmut Kandemir and Ding-Yong Hong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=g1HxCIc0wi}
} | Monte-Carlo tree search (MCTS) is an influential sequential decision-making algorithm notably employed in AlphaZero. Despite its success, the primary challenge in AlphaZero training lies in its prolonged time-to-solution due to the high latency imposed by the sequential MCTS process. To address this challenge, this paper proposes and evaluates an inter-decision parallelization strategy called speculative MCTS, a new type of parallelism in AlphaZero which implements speculative execution. This approach allows for the parallel execution of future moves before the current MCTS computations are completed, thus reducing the latency. Additionally, we analyze factors contributing to the overall speedup by studying the synergistic effects of speculation and neural network caching in MCTS. We also provide an analytical model that can be used to evaluate the potential of different speculation strategies before they are implemented and deployed. Our empirical findings indicate that the proposed speculative MCTS can reduce training latency by 5.81$\times$ in 9x9 Go games. Moreover, our study shows that speculative execution can enhance the NN cache hit rate by 26\% during midgame. Overall, our end-to-end evaluation indicates 1.91$\times$ speedup in 19x19 Go training time, compared to the state-of-the-art KataGo program. | Speculative Monte-Carlo Tree Search | [
"Scott Cheng",
"Mahmut Kandemir",
"Ding-Yong Hong"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=g0G8DQSBcj | @inproceedings{
ghari2024gflownet,
title={{GF}lowNet Assisted Biological Sequence Editing},
author={Pouya M. Ghari and Alex M Tseng and G{\"o}kcen Eraslan and Romain Lopez and Tommaso Biancalani and Gabriele Scalia and Ehsan Hajiramezanali},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=g0G8DQSBcj}
} | Editing biological sequences has extensive applications in synthetic biology and medicine, such as designing regulatory elements for nucleic-acid therapeutics and treating genetic disorders. The primary objective in biological-sequence editing is to determine the optimal modifications to a sequence which augment certain biological properties while adhering to a minimal number of alterations to ensure predictability and potentially support safety. In this paper, we propose GFNSeqEditor, a novel biological-sequence editing algorithm which builds on the recently proposed area of generative flow networks (GFlowNets). Our proposed GFNSeqEditor identifies elements within a starting seed sequence that may compromise a desired biological property. Then, using a learned stochastic policy, the algorithm makes edits at these identified locations, offering diverse modifications for each sequence to enhance the desired property. The number of edits can be regulated through specific hyperparameters. We conducted extensive experiments on a range of real-world datasets and biological applications, and our results underscore the superior performance of our proposed algorithm compared to existing state-of-the-art sequence editing methods. | GFlowNet Assisted Biological Sequence Editing | [
"Pouya M. Ghari",
"Alex M Tseng",
"Gökcen Eraslan",
"Romain Lopez",
"Tommaso Biancalani",
"Gabriele Scalia",
"Ehsan Hajiramezanali"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=fzlMza6dRZ | @inproceedings{
armgaan2024graphtrail,
title={GraphTrail: Translating {GNN} Predictions into Human-Interpretable Logical Rules},
author={Burouj Armgaan and Manthan Dalmia and Sourav Medya and Sayan Ranu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fzlMza6dRZ}
} | Instance-level explanation of graph neural networks (GNNs) is a well-studied area. These explainers, however, only explain an instance (e.g., a graph) and fail to uncover the combinatorial reasoning learned by a GNN from the training data towards making its predictions. In this work, we introduce GraphTrail, the first end-to-end, global, post-hoc GNN explainer that translates the functioning of a black-box GNN model to a boolean formula over the (sub)graph level concepts without relying on local explainers. GraphTrail is unique in automatically mining the discriminative subgraph-level concepts using Shapley values. Subsequently, the GNN predictions are mapped to a human-interpretable boolean formula over these concepts through symbolic regression. Extensive experiments across diverse datasets and GNN architectures demonstrate significant improvement over existing global explainers in mapping GNN predictions to faithful logical formulae. The robust and accurate performance of GraphTrail makes it invaluable for improving GNNs and facilitates adoption in domains with strict transparency requirements. | GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules | [
"Burouj Armgaan",
"Manthan Dalmia",
"Sourav Medya",
"Sayan Ranu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=fymr0CBDHZ | @inproceedings{
zhu2024slim,
title={{SLIM}: Style-Linguistics Mismatch Model for Generalized Audio Deepfake Detection},
author={Yi Zhu and Surya Koppisetti and Trang Tran and Gaurav Bharaj},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fymr0CBDHZ}
} | Audio deepfake detection (ADD) is crucial to combat the misuse of speech synthesized by generative AI models. Existing ADD models suffer from generalization issues to unseen attacks, with a large performance discrepancy between in-domain and out-of-domain data. Moreover, the black-box nature of existing models limits their use in real-world scenarios, where explanations are required for model decisions. To alleviate these issues, we introduce a new ADD model that explicitly uses the Style-LInguistics Mismatch (SLIM) in fake speech to separate them from real speech. SLIM first employs self-supervised pretraining on only real samples to learn the style-linguistics dependency in the real class. The learned features are then used in complement with standard pretrained acoustic features (e.g., Wav2vec) to learn a classifier on the real and fake classes. When the feature encoders are frozen, SLIM outperforms benchmark methods on out-of-domain datasets while achieving competitive results on in-domain data. The features learned by SLIM allow us to quantify the (mis)match between style and linguistic content in a sample, hence facilitating an explanation of the model decision. | SLIM: Style-Linguistics Mismatch Model for Generalized Audio Deepfake Detection | [
"Yi Zhu",
"Surya Koppisetti",
"Trang Tran",
"Gaurav Bharaj"
] | NeurIPS.cc/2024/Conference | 2407.18517 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fykjplMc0V | @inproceedings{
wu2024reft,
title={Re{FT}: Representation Finetuning for Language Models},
author={Zhengxuan Wu and Aryaman Arora and Zheng Wang and Atticus Geiger and Dan Jurafsky and Christopher D Manning and Christopher Potts},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fykjplMc0V}
} | Parameter-efficient finetuning (PEFT) methods seek to adapt large neural models via updates to a small number of *weights*. However, much prior interpretability work has shown that *representations* encode rich semantic information, suggesting that editing representations might be a more powerful alternative. We pursue this hypothesis by developing a family of **Representation Finetuning (ReFT)** methods. ReFT methods operate on a frozen base model and learn task-specific interventions on hidden representations. We define a strong instance of the ReFT family, Low-rank Linear Subspace ReFT (LoReFT), and we identify an ablation of this method that trades some performance for increased efficiency. Both are drop-in replacements for existing PEFTs and learn interventions that are 15x--65x more parameter-efficient than LoRA. We showcase LoReFT on eight commonsense reasoning tasks, four arithmetic reasoning tasks, instruction-tuning, and GLUE. In all these evaluations, our ReFTs deliver the best balance of efficiency and performance, and almost always outperform state-of-the-art PEFTs. Upon publication, we will publicly release our generic ReFT training library. | ReFT: Representation Finetuning for Language Models | [
"Zhengxuan Wu",
"Aryaman Arora",
"Zheng Wang",
"Atticus Geiger",
"Dan Jurafsky",
"Christopher D Manning",
"Christopher Potts"
] | NeurIPS.cc/2024/Conference | 2404.03592 | [
"https://github.com/stanfordnlp/pyreft"
] | https://huggingface.co/papers/2404.03592 | 4 | 90 | 4 | 7 | [
"nenad1002/quantum-research-bot-v1.0"
] | [] | [
"pyvene/reft_chat7b_1k",
"pyvene/reft_golden_gate_bridge_llama3",
"pyvene/reft_ethos",
"pyvene/reft_ethos_llama3"
] | [
"nenad1002/quantum-research-bot-v1.0"
] | [] | [
"pyvene/reft_chat7b_1k",
"pyvene/reft_golden_gate_bridge_llama3",
"pyvene/reft_ethos",
"pyvene/reft_ethos_llama3"
] | 1 | oral |
null | https://openreview.net/forum?id=fyYrZbWtNz | @inproceedings{
yu2024rethinking,
title={Rethinking Imbalance in Image Super-Resolution for Efficient Inference},
author={Wei Yu and Bowen Yang and Qinglin Liu and Jianing Li and Shengping Zhang and Xiangyang Ji},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fyYrZbWtNz}
} | Existing super-resolution (SR) methods optimize all model weights equally using $\mathcal{L}_1$ or $\mathcal{L}_2$ losses by uniformly sampling image patches without considering dataset imbalances or parameter redundancy, which limits their performance. To address this, we formulate the image SR task as an imbalanced distribution transfer learning problem from a statistical probability perspective, proposing a plug-and-play Weight-Balancing framework (WBSR) to achieve balanced model learning without changing the original model structure and training data. Specifically, we develop a Hierarchical Equalization Sampling (HES) strategy to address data distribution imbalances, enabling better feature representation from texture-rich samples. To tackle model optimization imbalances, we propose a Balanced Diversity Loss (BDLoss) function, focusing on learning texture regions while disregarding redundant computations in smooth regions. After joint training of HES and BDLoss to rectify these imbalances, we present a gradient projection dynamic inference strategy to facilitate accurate and efficient inference. Extensive experiments across various models, datasets, and scale factors demonstrate that our method achieves comparable or superior performance to existing approaches with about 34\% reduction in computational cost. | Rethinking Imbalance in Image Super-Resolution for Efficient Inference | [
"Wei Yu",
"Bowen Yang",
"Qinglin Liu",
"Jianing Li",
"Shengping Zhang",
"Xiangyang Ji"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=fx6aSBMu6z | @inproceedings{
bai2024hcgae,
title={{HC}-{GAE}: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning},
author={Lu Bai and Zhuo Xu and Lixin Cui and Ming Li and Yue Wang and Edwin Hancock},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fx6aSBMu6z}
} | Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph data analysis. To this end, during the encoding process, we commence by utilizing the hard node assignment to decompose a sample graph into a family of separated subgraphs. We compress each subgraph into a coarsened node, transforming the original graph into a coarsened graph. On the other hand, during the decoding process, we adopt the soft node assignment to reconstruct the original graph structure by expanding the coarsened nodes. By hierarchically performing the above compressing procedure during the decoding process as well as the expanding procedure during the decoding process, the proposed HC-GAE can effectively extract bidirectionally hierarchical structural features of the original sample graph. Furthermore, we re-design the loss function that can integrate the information from either the encoder or the decoder. Since the associated graph convolution operation of the proposed HC-GAE is restricted in each individual separated subgraph and cannot propagate the node information between different subgraphs, the proposed HC-GAE can significantly reduce the over-smoothing problem arising in the classical convolution-based GAEs. The proposed HC-GAE can generate effective representations for either node classification or graph classification, and the experiments demonstrate the effectiveness on real-world datasets. | HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning | [
"Lu Bai",
"Zhuo Xu",
"Lixin Cui",
"Ming Li",
"Yue Wang",
"Edwin Hancock"
] | NeurIPS.cc/2024/Conference | 2405.14742 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fvOCJAAYLx | @inproceedings{
mercatali2024diffusion,
title={Diffusion Twigs with Loop Guidance for Conditional Graph Generation},
author={Giangiacomo Mercatali and Yogesh Verma and Andre Freitas and Vikas Garg},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fvOCJAAYLx}
} | We introduce a novel score-based diffusion framework named Twigs that incorporates multiple co-evolving flows for enriching conditional generation tasks. Specifically, a central or trunk diffusion process is associated with a primary variable (e.g., graph structure), and additional offshoot or stem processes are dedicated to dependent variables (e.g., graph properties or labels). A new strategy, which we call loop guidance, effectively orchestrates the flow of information between the trunk and the stem processes during sampling. This approach allows us to uncover intricate interactions and dependencies, and unlock new generative capabilities. We provide extensive experiments to demonstrate strong performance gains of the proposed method over contemporary baselines in the context of conditional graph generation, underscoring the potential of Twigs in challenging generative tasks such as inverse molecular design and molecular optimization.
Code is available at https://github.com/Aalto-QuML/Diffusion_twigs. | Diffusion Twigs with Loop Guidance for Conditional Graph Generation | [
"Giangiacomo Mercatali",
"Yogesh Verma",
"Andre Freitas",
"Vikas Garg"
] | NeurIPS.cc/2024/Conference | 2410.24012 | [
"https://github.com/aalto-quml/diffusion_twigs"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fvG6ZHrH0B | @inproceedings{
s{\'a}godi2024back,
title={Back to the Continuous Attractor},
author={{\'A}bel S{\'a}godi and Guillermo Mart{\'\i}n-S{\'a}nchez and Piotr A Sokol and Il Memming Park},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fvG6ZHrH0B}
} | Continuous attractors offer a unique class of solutions for storing continuous-valued variables in recurrent system states for indefinitely long time intervals.
Unfortunately, continuous attractors suffer from severe structural instability in general---they are destroyed by most infinitesimal changes of the dynamical law that defines them.
This fragility limits their utility especially in biological systems as their recurrent dynamics are subject to constant perturbations.
We observe that the bifurcations from continuous attractors in theoretical neuroscience models display various structurally stable forms.
Although their asymptotic behaviors to maintain memory are categorically distinct, their finite-time behaviors are similar.
We build on the persistent manifold theory to explain the commonalities between bifurcations from and approximations of continuous attractors.
Fast-slow decomposition analysis uncovers the existence of a persistent slow manifold that survives the seemingly destructive bifurcation, relating the flow within the manifold to the size of the perturbation. Moreover, this allows the bounding of the memory error of these approximations of continuous attractors.
Finally, we train recurrent neural networks on analog memory tasks to support the appearance of these systems as solutions and their generalization capabilities.
Therefore, we conclude that continuous attractors are functionally robust and remain useful as a universal analogy for understanding analog memory. | Back to the Continuous Attractor | [
"Ábel Ságodi",
"Guillermo Martín-Sánchez",
"Piotr A Sokol",
"Il Memming Park"
] | NeurIPS.cc/2024/Conference | 2408.00109 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fu0xdh4aEJ | @inproceedings{
nauman2024bigger,
title={Bigger, Regularized, Optimistic: scaling for compute and sample efficient continuous control},
author={Michal Nauman and Mateusz Ostaszewski and Krzysztof Jankowski and Piotr Mi{\l}o{\'s} and Marek Cygan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fu0xdh4aEJ}
} | Sample efficiency in Reinforcement Learning (RL) has traditionally been driven by algorithmic enhancements. In this work, we demonstrate that scaling can also lead to substantial improvements. We conduct a thorough investigation into the interplay of scaling model capacity and domain-specific RL enhancements. These empirical findings inform the design choices underlying our proposed BRO (Bigger, Regularized, Optimistic) algorithm. The key innovation behind BRO is that strong regularization allows for effective scaling of the critic networks, which, paired with optimistic exploration, leads to superior performance. BRO achieves state-of-the-art results, significantly outperforming the leading model-based and model-free algorithms across 40 complex tasks from the DeepMind Control, MetaWorld, and MyoSuite benchmarks. BRO is the first model-free algorithm to achieve near-optimal policies in the notoriously challenging Dog and Humanoid tasks. | Bigger, Regularized, Optimistic: scaling for compute and sample efficient continuous control | [
"Michal Nauman",
"Mateusz Ostaszewski",
"Krzysztof Jankowski",
"Piotr Miłoś",
"Marek Cygan"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=ftqjwZQz10 | @inproceedings{
gong2024dex,
title={{DEX}: Data Channel Extension for Efficient {CNN} Inference on Tiny {AI} Accelerators},
author={Taesik Gong and Fahim Kawsar and Chulhong Min},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ftqjwZQz10}
} | Tiny machine learning (TinyML) aims to run ML models on small devices and is increasingly favored for its enhanced privacy, reduced latency, and low cost. Recently, the advent of tiny AI accelerators has revolutionized the TinyML field by significantly enhancing hardware processing power. These accelerators, equipped with multiple parallel processors and dedicated per-processor memory instances, offer substantial performance improvements over traditional microcontroller units (MCUs). However, their limited data memory often necessitates downsampling input images, resulting in accuracy degradation. To address this challenge, we propose Data channel EXtension (DEX), a novel approach for efficient CNN execution on tiny AI accelerators. DEX incorporates additional spatial information from original images into input images through patch-wise even sampling and channel-wise stacking, effectively extending data across input channels. By leveraging underutilized processors and data memory for channel extension, DEX facilitates parallel execution without increasing inference latency. Our evaluation with four models and four datasets on tiny AI accelerators demonstrates that this simple idea improves accuracy on average by 3.5%p while keeping the inference latency the same on the AI accelerator. The source code is available at https://github.com/Nokia-Bell-Labs/data-channel-extension. | DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators | [
"Taesik Gong",
"Fahim Kawsar",
"Chulhong Min"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=fs28jccJj5 | @inproceedings{
hwang2024spikedattention,
title={SpikedAttention: Training-Free and Fully Spike-Driven Transformer-to-{SNN} Conversion with Winner-Oriented Spike Shift for Softmax Operation},
author={Sangwoo Hwang and Seunghyun Lee and Dahoon Park and Donghun Lee and Jaeha Kung},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fs28jccJj5}
} | Event-driven spiking neural networks(SNNs) are promising neural networks that reduce the energy consumption of continuously growing AI models. Recently, keeping pace with the development of transformers, transformer-based SNNs were presented. Due to the incompatibility of self-attention with spikes, however, existing transformer-based SNNs limit themselves by either restructuring self-attention architecture or conforming to non-spike computations. In this work, we propose a novel transformer-to-SNN conversion method that outputs an end-to-end spike-based transformer, named SpikedAttention. Our method directly converts the well-trained transformer without modifying its attention architecture. For the vision task, the proposed method converts Swin Transformer into an SNN without post-training or conversion-aware training, achieving state-of-the-art SNN accuracy on ImageNet dataset, i.e., 80.0\% with 28.7M parameters. Considering weight accumulation, neuron potential update, and on-chip data movement, SpikedAttention reduces energy consumption by 42\% compared to the baseline ANN, i.e., Swin-T. Furthermore, for the first time, we demonstrate that SpikedAttention successfully converts a BERT model to an SNN with only 0.3\% accuracy loss on average consuming 58\% less energy on GLUE benchmark. Our code is available at Github ( https://github.com/sangwoohwang/SpikedAttention ). | SpikedAttention: Training-Free and Fully Spike-Driven Transformer-to-SNN Conversion with Winner-Oriented Spike Shift for Softmax Operation | [
"Sangwoo Hwang",
"Seunghyun Lee",
"Dahoon Park",
"Donghun Lee",
"Jaeha Kung"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=fqmSGK8C0B | @inproceedings{
qu2024deep,
title={Deep Learning for Computing Convergence Rates of Markov Chains},
author={Yanlin Qu and Jose Blanchet and Peter Glynn},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fqmSGK8C0B}
} | Convergence rate analysis for general state-space Markov chains is fundamentally important in operations research (stochastic systems) and machine learning (stochastic optimization). This problem, however, is notoriously difficult because traditional analytical methods often do not generate practically useful convergence bounds for realistic Markov chains. We propose the Deep Contractive Drift Calculator (DCDC), the first general-purpose sample-based algorithm for bounding the convergence of Markov chains to stationarity in Wasserstein distance. The DCDC has two components. First, inspired by the new convergence analysis framework in (Qu et.al, 2023), we introduce the Contractive Drift Equation (CDE), the solution of which leads to an explicit convergence bound. Second, we develop an efficient neural-network-based CDE solver. Equipped with these two components, DCDC solves the CDE and converts the solution into a convergence bound. We analyze the sample complexity of the algorithm and further demonstrate the effectiveness of the DCDC by generating convergence bounds for realistic Markov chains arising from stochastic processing networks as well as constant step-size stochastic optimization. | Deep Learning for Computing Convergence Rates of Markov Chains | [
"Yanlin Qu",
"Jose Blanchet",
"Peter Glynn"
] | NeurIPS.cc/2024/Conference | 2405.20435 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=fqjeKsHOVR | @inproceedings{
zhao2024harmonizing,
title={Harmonizing Visual Text Comprehension and Generation},
author={Zhen Zhao and Jingqun Tang and Binghong Wu and Chunhui Lin and Shu Wei and Hao Liu and Xin Tan and zhizhong zhang and Can Huang and Yuan Xie},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fqjeKsHOVR}
} | In this work, we present TextHarmony, a unified and versatile multimodal generative model proficient in comprehending and generating visual text. Simultaneously generating images and texts typically results in performance degradation due to the inherent inconsistency between vision and language modalities. To overcome this challenge, existing approaches resort to modality-specific data for supervised fine-tuning, necessitating distinct model instances. We propose Slide-LoRA, which dynamically aggregates modality-specific and modality-agnostic LoRA experts, partially decoupling the multimodal generation space. Slide-LoRA harmonizes the generation of vision and language within a singular model instance, thereby facilitating a more unified generative process. Additionally, we develop a high-quality image caption dataset, DetailedTextCaps-100K, synthesized with a sophisticated closed-source MLLM to enhance visual text generation capabilities further. Comprehensive experiments across various benchmarks demonstrate the effectiveness of the proposed approach. Empowered by Slide-LoRA, TextHarmony achieves comparable performance to modality-specific fine-tuning results with only a 2% increase in parameters and shows an average improvement of 2.5% in visual text comprehension tasks and 4.0% in visual text generation tasks. Our work delineates the viability of an integrated approach to multimodal generation within the visual text domain, setting a foundation for subsequent inquiries. Code is available at https://github.com/bytedance/TextHarmony. | Harmonizing Visual Text Comprehension and Generation | [
"Zhen Zhao",
"Jingqun Tang",
"Binghong Wu",
"Chunhui Lin",
"Shu Wei",
"Hao Liu",
"Xin Tan",
"zhizhong zhang",
"Can Huang",
"Yuan Xie"
] | NeurIPS.cc/2024/Conference | 2407.16364 | [
"https://github.com/bytedance/textharmony"
] | https://huggingface.co/papers/2407.16364 | 0 | 0 | 0 | 10 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=fpxRpPbF1t | @inproceedings{
lee2024differentiable,
title={Differentiable Modal Synthesis for Physical Modeling of Planar String Sound and Motion Simulation},
author={Jin Woo Lee and Jaehyun Park and Min Jun Choi and Kyogu Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fpxRpPbF1t}
} | While significant advancements have been made in music generation and differentiable sound synthesis within machine learning and computer audition, the simulation of instrument vibration guided by physical laws has been underexplored. To address this gap, we introduce a novel model for simulating the spatio-temporal motion of nonlinear strings, integrating modal synthesis and spectral modeling within a neural network framework. Our model leverages mechanical properties and fundamental frequencies as inputs, outputting string states across time and space that solve the partial differential equation characterizing the nonlinear string. Empirical evaluations demonstrate that the proposed architecture achieves superior accuracy in string motion simulation compared to existing baseline architectures. The code and demo are available online. | Differentiable Modal Synthesis for Physical Modeling of Planar String Sound and Motion Simulation | [
"Jin Woo Lee",
"Jaehyun Park",
"Min Jun Choi",
"Kyogu Lee"
] | NeurIPS.cc/2024/Conference | 2407.05516 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fpOnUMjLiO | @inproceedings{
zhao2024theoretical,
title={Theoretical Characterisation of the Gauss Newton Conditioning in Neural Networks},
author={Jim Zhao and Sidak Pal Singh and Aurelien Lucchi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fpOnUMjLiO}
} | The Gauss-Newton (GN) matrix plays an important role in machine learning, most evident in its use as a preconditioning matrix for a wide family of popular adaptive methods to speed up optimization. Besides, it can also provide key insights into the optimization landscape of neural networks.
In the context of deep neural networks, understanding the GN matrix involves studying the interaction between different weight matrices as well as the dependencies introduced by the data, thus rendering its analysis challenging.
In this work, we take a first step towards theoretically characterizing the conditioning of the GN matrix in neural networks. We establish tight bounds on the condition number of the GN in deep linear networks of arbitrary depth and width, which we also extend to two-layer ReLU networks.
We expand the analysis to further architectural components, such as residual connections and convolutional layers.
Finally, we empirically validate the bounds and uncover valuable insights into the influence of the analyzed architectural components. | Theoretical Characterisation of the Gauss Newton Conditioning in Neural Networks | [
"Jim Zhao",
"Sidak Pal Singh",
"Aurelien Lucchi"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=fogJgrozu1 | @inproceedings{
zecchin2024localized,
title={Localized Adaptive Risk Control},
author={Matteo Zecchin and Osvaldo Simeone},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fogJgrozu1}
} | Adaptive Risk Control (ARC) is an online calibration strategy based on set prediction that offers worst-case deterministic long-term risk control, as well as statistical marginal coverage guarantees. ARC adjusts the size of the prediction set by varying a single scalar threshold based on feedback from past decisions. In this work, we introduce Localized Adaptive Risk Control (L-ARC), an online calibration scheme that targets statistical localized risk guarantees ranging from conditional risk to marginal risk, while preserving the worst-case performance of ARC. L-ARC updates a threshold function within a reproducing kernel Hilbert space (RKHS), with the kernel determining the level of localization of the statistical risk guarantee. The theoretical results highlight a trade-off between localization of the statistical risk and convergence speed to the long-term risk target. Thanks to localization, L-ARC is demonstrated via experiments to produce prediction sets with risk guarantees across different data subpopulations, significantly improving the fairness of the calibrated model for tasks such as image segmentation and beam selection in wireless networks. | Localized Adaptive Risk Control | [
"Matteo Zecchin",
"Osvaldo Simeone"
] | NeurIPS.cc/2024/Conference | 2405.07976 | [
"https://github.com/kclip/localized-adaptive-risk-control"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fkuseU0nJs | @inproceedings{
griesemer2024active,
title={Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference},
author={Sam Griesemer and Defu Cao and Zijun Cui and Carolina Osorio and Yan Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fkuseU0nJs}
} | Computer simulations have long presented the exciting possibility of scientific insight into complex real-world processes. Despite the power of modern computing, however, it remains challenging to systematically perform inference under simulation models. This has led to the rise of simulation-based inference (SBI), a class of machine learning-enabled techniques for approaching inverse problems with stochastic simulators. Many such methods, however, require large numbers of simulation samples and face difficulty scaling to high-dimensional settings, often making inference prohibitive under resource-intensive simulators. To mitigate these drawbacks, we introduce active sequential neural posterior estimation (ASNPE). ASNPE brings an active learning scheme into the inference loop to estimate the utility of simulation parameter candidates to the underlying probabilistic model. The proposed acquisition scheme is easily integrated into existing posterior estimation pipelines, allowing for improved sample efficiency with low computational overhead. We further demonstrate the effectiveness of the proposed method in the travel demand calibration setting, a high-dimensional inverse problem commonly requiring computationally expensive traffic simulators. Our method outperforms well-tuned benchmarks and state-of-the-art posterior estimation methods on a large-scale real-world traffic network, as well as demonstrates a performance advantage over non-active counterparts on a suite of SBI benchmark environments. | Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference | [
"Sam Griesemer",
"Defu Cao",
"Zijun Cui",
"Carolina Osorio",
"Yan Liu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=fkf0OquD3Q | @inproceedings{
asi2024private,
title={Private Online Learning via Lazy Algorithms},
author={Hilal Asi and Tomer Koren and Daogao Liu and Kunal Talwar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fkf0OquD3Q}
} | We study the problem of private online learning, specifically, online prediction from experts (OPE) and online convex optimization (OCO).
We propose a new transformation that transforms lazy online learning algorithms into private algorithms. We apply our transformation for differentially private OPE and OCO using existing lazy algorithms for these problems. Our final algorithms obtain regret which significantly improves the regret in the high privacy regime $\varepsilon \ll 1$, obtaining $\sqrt{T \log d} + T^{1/3} \log(d)/\varepsilon^{2/3}$ for DP-OPE and $\sqrt{T} + T^{1/3} \sqrt{d}/\varepsilon^{2/3}$ for DP-OCO. We also complement our results with a lower bound for DP-OPE, showing that these rates are optimal for a natural family of low-switching private algorithms. | Private Online Learning via Lazy Algorithms | [
"Hilal Asi",
"Tomer Koren",
"Daogao Liu",
"Kunal Talwar"
] | NeurIPS.cc/2024/Conference | 2406.03620 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fkbMlfDBxm | @inproceedings{
chen2024reconstruct,
title={Reconstruct and Match: Out-of-Distribution Robustness via Topological Homogeneity},
author={Chaoqi Chen and Luyao Tang and Hui Huang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fkbMlfDBxm}
} | Since deep learning models are usually deployed in non-stationary environments, it is imperative to improve their robustness to out-of-distribution (OOD) data. A common approach to mitigate distribution shift is to regularize internal representations or predictors learned from in-distribution (ID) data to be domain invariant. Past studies have primarily learned pairwise invariances, ignoring the intrinsic structure and high-order dependencies of the data. Unlike machines, human recognizes objects by first dividing them into major components and then identifying the topological relation of these components. Motivated by this, we propose Reconstruct and Match (REMA), a general learning framework for object recognition tasks to endow deep models with the capability of capturing the topological homogeneity of objects without human prior knowledge or fine-grained annotations. To identify major components from objects, REMA introduces a selective slot-based reconstruction module to dynamically map dense pixels into a sparse and discrete set of slot vectors in an unsupervised manner. Then, to model high-order dependencies among these components, we propose a hypergraph-based relational reasoning module that models the intricate relations of nodes (slots) with structural constraints. Experiments on standard benchmarks show that REMA outperforms state-of-the-art methods in OOD generalization and test-time adaptation settings. | Reconstruct and Match: Out-of-Distribution Robustness via Topological Homogeneity | [
"Chaoqi Chen",
"Luyao Tang",
"Hui Huang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=fjLCqicn64 | @inproceedings{
yu2024longrange,
title={Long-range Brain Graph Transformer},
author={Shuo Yu and Shan Jin and Ming Li and Tabinda Sarwar and Feng Xia},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fjLCqicn64}
} | Understanding communication and information processing among brain regions of interest (ROIs) is highly dependent on long-range connectivity, which plays a crucial role in facilitating diverse functional neural integration across the entire brain. However, previous studies generally focused on the short-range dependencies within brain networks while neglecting the long-range dependencies, limiting an integrated understanding of brain-wide communication. To address this limitation, we propose Adaptive Long-range aware TransformER (ALTER), a brain graph transformer to capture long-range dependencies between brain ROIs utilizing biased random walk. Specifically, we present a novel long-range aware strategy to explicitly capture long-range dependencies between brain ROIs. By guiding the walker towards the next hop with higher correlation value, our strategy simulates the real-world brain-wide communication. Furthermore, by employing the transformer framework, ALERT adaptively integrates both short- and long-range dependencies between brain ROIs, enabling an integrated understanding of multi-level communication across the entire brain. Extensive experiments on ABIDE and ADNI datasets demonstrate that ALTER consistently outperforms generalized state-of-the-art graph learning methods (including SAN, Graphormer, GraphTrans, and LRGNN) and other graph learning based brain network analysis methods (including FBNETGEN, BrainNetGNN, BrainGNN, and BrainNETTF) in neurological disease diagnosis. | Long-range Brain Graph Transformer | [
"Shuo Yu",
"Shan Jin",
"Ming Li",
"Tabinda Sarwar",
"Feng Xia"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=fi3aKVnBQo | @inproceedings{
bharadwaj2024efficient,
title={Efficient Leverage Score Sampling for Tensor Train Decomposition},
author={Vivek Bharadwaj and Beheshteh T. Rakhshan and Osman Asif Malik and Guillaume Rabusseau},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fi3aKVnBQo}
} | Tensor Train~(TT) decomposition is widely used in the machine learning and quantum physics communities as a popular tool to efficiently compress high-dimensional tensor data. In this paper, we propose an efficient algorithm to accelerate computing the TT decomposition with the Alternating Least Squares (ALS) algorithm relying on exact leverage scores sampling. For this purpose, we propose a data structure that allows us to efficiently sample from the tensor with time complexity logarithmic in the product of the tensor dimensions. Our contribution specifically leverages the canonical form of the TT decomposition. By maintaining the canonical form through each iteration of ALS, we can efficiently compute (and sample from) the leverage scores, thus achieving significant speed-up in solving each sketched least-square problem. Experiments on synthetic and real data on dense and sparse tensors demonstrate that our method outperforms SVD-based and ALS-based algorithms. | Efficient Leverage Score Sampling for Tensor Train Decomposition | [
"Vivek Bharadwaj",
"Beheshteh T. Rakhshan",
"Osman Asif Malik",
"Guillaume Rabusseau"
] | NeurIPS.cc/2024/Conference | 2406.02749 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ffeUBoTcdS | @inproceedings{
hoang2024persistent,
title={Persistent Test-time Adaptation in Recurring Testing Scenarios},
author={Trung-Hieu Hoang and MinhDuc Vo and Minh N. Do},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ffeUBoTcdS}
} | Current test-time adaptation (TTA) approaches aim to adapt a machine learning model to environments that change continuously. Yet, it is unclear whether TTA methods can maintain their adaptability over prolonged periods. To answer this question, we introduce a diagnostic setting - **recurring TTA** where environments not only change but also recur over time, creating an extensive data stream. This setting allows us to examine the error accumulation of TTA models, in the most basic scenario, when they are regularly exposed to previous testing environments. Furthermore, we simulate a TTA process on a simple yet representative $\epsilon$-**perturbed Gaussian Mixture Model Classifier**, deriving theoretical insights into the dataset- and algorithm-dependent factors contributing to gradual performance degradation. Our investigation leads us to propose **persistent TTA (PeTTA)**, which senses when the model is diverging towards collapse and adjusts the adaptation strategy, striking a balance between the dual objectives of adaptation and model collapse prevention. The supreme stability of PeTTA over existing approaches, in the face of lifelong TTA scenarios, has been demonstrated over comprehensive experiments on various benchmarks. Our project page is available at [https://hthieu166.github.io/petta](https://hthieu166.github.io/petta). | Persistent Test-time Adaptation in Recurring Testing Scenarios | [
"Trung-Hieu Hoang",
"MinhDuc Vo",
"Minh N. Do"
] | NeurIPS.cc/2024/Conference | 2311.18193 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ffNrpcBpi6 | @inproceedings{
choi2024graph,
title={Graph Convolutions Enrich the Self-Attention in Transformers!},
author={Jeongwhan Choi and Hyowon Wi and Jayoung Kim and Yehjin Shin and Kookjin Lee and Nathaniel Trask and Noseong Park},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ffNrpcBpi6}
} | Transformers, renowned for their self-attention mechanism, have achieved state-of-the-art performance across various tasks in natural language processing, computer vision, time-series modeling, etc. However, one of the challenges with deep Transformer models is the oversmoothing problem, where representations across layers converge to indistinguishable values, leading to significant performance degradation. We interpret the original self-attention as a simple graph filter and redesign it from a graph signal processing (GSP) perspective. We propose a graph-filter-based self-attention (GFSA) to learn a general yet effective one, whose complexity, however, is slightly larger than that of the original self-attention mechanism. We demonstrate that GFSA improves the performance of Transformers in various fields, including computer vision, natural language processing, graph-level tasks, speech recognition, and code classification. | Graph Convolutions Enrich the Self-Attention in Transformers! | [
"Jeongwhan Choi",
"Hyowon Wi",
"Jayoung Kim",
"Yehjin Shin",
"Kookjin Lee",
"Nathaniel Trask",
"Noseong Park"
] | NeurIPS.cc/2024/Conference | 2312.04234 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ferj6WqShv | @inproceedings{
luo2024exploiting,
title={Exploiting Descriptive Completeness Prior for Cross Modal Hashing with Incomplete Labels},
author={Haoyang Luo and Zheng Zhang and Yadan Luo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ferj6WqShv}
} | In this paper, we tackle the challenge of generating high-quality hash codes for cross-modal retrieval in the presence of incomplete labels, which creates uncertainty in distinguishing between positive and negative pairs. Vision-language models such as CLIP offer a potential solution by providing generic knowledge for missing label recovery, yet their zero-shot performance remains insufficient. To address this, we propose a novel Prompt Contrastive Recovery approach, \textbf{PCRIL}, which progressively identifies promising positive classes from unknown label sets and recursively searches for other relevant labels. Identifying unknowns is nontrivial due to the fixed and long-tailed patterns of positive label sets in training data, which hampers the discovery of new label combinations. Therefore, we consider each subset of positive labels and construct three types of negative prompts through deletion, addition, and replacement for prompt learning. The augmented supervision guides the model to measure the completeness of label sets, thus facilitating the subsequent greedy tree search for label completion. We also address extreme cases of significant unknown labels and lack of negative pairwise supervision by deriving two augmentation strategies: seeking unknown-complementary samples for mixup and random flipping for negative labels. Extensive experiments reveal the vulnerability of current methods and demonstrate the effectiveness of PCRIL, achieving an average 12\% mAP improvement to the current SOTA across all datasets. Our code is available at https://github.com/E-Galois/PCRIL. | Exploiting Descriptive Completeness Prior for Cross Modal Hashing with Incomplete Labels | [
"Haoyang Luo",
"Zheng Zhang",
"Yadan Luo"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=fc88ANWvdF | @inproceedings{
potapczynski2024searching,
title={Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices},
author={Andres Potapczynski and Shikai Qiu and Marc Anton Finzi and Christopher Ferri and Zixi Chen and Micah Goldblum and C. Bayan Bruss and Christopher De Sa and Andrew Gordon Wilson},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fc88ANWvdF}
} | Dense linear layers are the dominant computational bottleneck in large neural networks, presenting a critical need for more efficient alternatives. Previous efforts to develop alternatives have focused on a small number of hand-crafted structured matrices, and have neglected to investigate whether these structures can surpass dense layers in terms of compute-optimal scaling laws when both the model size and training examples are optimally allocated. In this work, we present a unifying framework that enables searching among all linear operators expressible via an Einstein summation. This framework encompasses many previously proposed structures, such as low-rank, Kronecker, Tensor-Train, and Monarch, along with many novel structures. We develop a taxonomy of all such operators based on their computational and algebraic properties, which provides insights into their scaling laws. Combining these insights with empirical evaluation, we identify a subset of structures that achieve equal or better performance than dense layers as a function of training compute. To further improve their compute efficiency, we develop a natural extension of these performant structures that convert them into a sparse Mixture-of-Experts layer. The resulting layer significantly outperforms dense layers in compute-optimal training efficiency for GPT-2 language models. | Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices | [
"Andres Potapczynski",
"Shikai Qiu",
"Marc Anton Finzi",
"Christopher Ferri",
"Zixi Chen",
"Micah Goldblum",
"C. Bayan Bruss",
"Christopher De Sa",
"Andrew Gordon Wilson"
] | NeurIPS.cc/2024/Conference | 2410.02117 | [
"https://github.com/andpotap/einsum-search"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=faj2EBhdHC | @inproceedings{
skryagin2024graph,
title={Graph Neural Networks Need Cluster-Normalize-Activate Modules},
author={Arseny Skryagin and Felix Divo and Mohammad Amin Ali and Devendra Singh Dhami and Kristian Kersting},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=faj2EBhdHC}
} | Graph Neural Networks (GNNs) are non-Euclidean deep learning models for graph-structured data. Despite their successful and diverse applications, oversmoothing prohibits deep architectures due to node features converging to a single fixed point. This severely limits their potential to solve complex tasks. To counteract this tendency, we propose a plug-and-play module consisting of three steps: Cluster→Normalize→Activate (CNA). By applying CNA modules, GNNs search and form super nodes in each layer, which are normalized and activated individually. We demonstrate in node classification and property prediction tasks that CNA significantly improves the accuracy over the state-of-the-art. Particularly, CNA reaches 94.18% and 95.75% accuracy on Cora and CiteSeer, respectively. It further benefits GNNs in regression tasks as well, reducing the mean squared error compared to all baselines. At the same time, GNNs with CNA require substantially fewer learnable parameters than competing architectures. | Graph Neural Networks Need Cluster-Normalize-Activate Modules | [
"Arseny Skryagin",
"Felix Divo",
"Mohammad Amin Ali",
"Devendra Singh Dhami",
"Kristian Kersting"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=faBXeVBNqz | @inproceedings{
zaverkin2024higherrank,
title={Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing},
author={Viktor Zaverkin and Francesco Alesiani and Takashi Maruyama and Federico Errica and Henrik Christiansen and Makoto Takamoto and Nicolas Weber and Mathias Niepert},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=faBXeVBNqz}
} | The ability to perform fast and accurate atomistic simulations is crucial for advancing the chemical sciences. By learning from high-quality data, machine-learned interatomic potentials achieve accuracy on par with ab initio and first-principles methods at a fraction of their computational cost. The success of machine-learned interatomic potentials arises from integrating inductive biases such as equivariance to group actions on an atomic system, e.g., equivariance to rotations and reflections. In particular, the field has notably advanced with the emergence of equivariant message passing. Most of these models represent an atomic system using spherical tensors, tensor products of which require complicated numerical coefficients and can be computationally demanding. Cartesian tensors offer a promising alternative, though state-of-the-art methods lack flexibility in message-passing mechanisms, restricting their architectures and expressive power. This work explores higher-rank irreducible Cartesian tensors to address these limitations. We integrate irreducible Cartesian tensor products into message-passing neural networks and prove the equivariance and traceless property of the resulting layers. Through empirical evaluations on various benchmark data sets, we consistently observe on-par or better performance than that of state-of-the-art spherical and Cartesian models. | Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing | [
"Viktor Zaverkin",
"Francesco Alesiani",
"Takashi Maruyama",
"Federico Errica",
"Henrik Christiansen",
"Makoto Takamoto",
"Nicolas Weber",
"Mathias Niepert"
] | NeurIPS.cc/2024/Conference | 2405.14253 | [
"https://github.com/nec-research/ictp"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fYfliutfHX | @inproceedings{
niu2024learning,
title={Learning predictable and robust neural representations by straightening image sequences},
author={Xueyan Niu and Cristina Savin and Eero P Simoncelli},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fYfliutfHX}
} | Prediction is a fundamental capability of all living organisms, and has been proposed as an objective for learning sensory representations. Recent work demonstrates that in primate visual systems, prediction is facilitated by neural representations that follow straighter temporal trajectories than their initial photoreceptor encoding, which allows for prediction by linear extrapolation. Inspired by these experimental findings, we develop a self-supervised learning (SSL) objective that explicitly quantifies and promotes straightening. We demonstrate the power of this objective in training deep feedforward neural networks on smoothly-rendered synthetic image sequences that mimic commonly-occurring properties of natural videos. The learned model contains neural embeddings that are predictive, but also factorize the geometric, photometric, and semantic attributes of objects. The representations also prove more robust to noise and adversarial attacks compared to previous SSL methods that optimize for invariance to random augmentations. Moreover, these beneficial properties can be transferred to other training procedures by using the straightening objective as a regularizer, suggesting a broader utility for straightening as a principle for robust unsupervised learning. | Learning predictable and robust neural representations by straightening image sequences | [
"Xueyan Niu",
"Cristina Savin",
"Eero P Simoncelli"
] | NeurIPS.cc/2024/Conference | 2411.01777 | [
"https://github.com/xyniu1/learning-by-straightening"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fYa6ezMxD5 | @inproceedings{
devvrit2024matformer,
title={MatFormer: Nested Transformer for Elastic Inference},
author={Fnu Devvrit and Sneha Kudugunta and Aditya Kusupati and Tim Dettmers and Kaifeng Chen and Inderjit S Dhillon and Yulia Tsvetkov and Hannaneh Hajishirzi and Sham M. Kakade and Ali Farhadi and Prateek Jain},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fYa6ezMxD5}
} | Foundation models are applied in a broad spectrum of settings with different inference constraints, from massive multi-accelerator clusters to resource-constrained standalone mobile devices. However, the substantial costs associated with training these models often limit the number of unique model sizes that can be offered. Consequently, practitioners are compelled to select a model that may not be optimally aligned with their specific latency and cost requirements. We present MatFormer, a novel Transformer architecture designed to provide elastic inference across diverse deployment constraints. MatFormer achieves this by incorporating a nested Feed Forward Network (FFN) block structure within a standard Transformer model. During training, we optimize the parameters of multiple nested FFN blocks with varying sizes, enabling the extraction of hundreds of accurate smaller models without incurring additional computational costs. We empirically validate the efficacy of MatFormer across different model classes (decoders and encoders) and modalities (language and vision), demonstrating its potential for real-world deployment. We show that a 850M decoder-only MatFormer language model (MatLM) allows us to extract multiple smaller models spanning from 582M to 850M parameters, each exhibiting better validation loss and one-shot downstream evaluations than independently trained counterparts. Furthermore, we observe that smaller encoders extracted from a universal MatFormer-based ViT (MatViT) encoder preserve the metric-space structure for adaptive large-scale retrieval. Finally, we showcase that speculative decoding with the accurate and consistent submodels extracted from MatFormer can lead to significant reduction in inference latency. | MatFormer: Nested Transformer for Elastic Inference | [
"Fnu Devvrit",
"Sneha Kudugunta",
"Aditya Kusupati",
"Tim Dettmers",
"Kaifeng Chen",
"Inderjit S Dhillon",
"Yulia Tsvetkov",
"Hannaneh Hajishirzi",
"Sham M. Kakade",
"Ali Farhadi",
"Prateek Jain"
] | NeurIPS.cc/2024/Conference | 2310.07707 | [
""
] | https://huggingface.co/papers/2310.07707 | 1 | 1 | 0 | 11 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=fXEi3LVflp | @inproceedings{
miao2024referring,
title={Referring Human Pose and Mask Estimation In the Wild},
author={Bo Miao and Mingtao Feng and Zijie Wu and Mohammed Bennamoun and Yongsheng Gao and Ajmal Saeed Mian},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fXEi3LVflp}
} | We introduce Referring Human Pose and Mask Estimation (R-HPM) in the wild, where either a text or positional prompt specifies the person of interest in an image. This new task holds significant potential for human-centric applications such as assistive robotics and sports analysis. In contrast to previous works, R-HPM (i) ensures high-quality, identity-aware results corresponding to the referred person, and (ii) simultaneously predicts human pose and mask for a comprehensive representation. To achieve this, we introduce a large-scale dataset named RefHuman, which substantially extends the MS COCO dataset with additional text and positional prompt annotations. RefHuman includes over 50,000 annotated instances in the wild, each equipped with keypoint, mask, and prompt annotations. To enable prompt-conditioned estimation, we propose the first end-to-end promptable approach named UniPHD for R-HPM. UniPHD extracts multimodal representations and employs a proposed pose-centric hierarchical decoder to process (text or positional) instance queries and keypoint queries, producing results specific to the referred person. Extensive experiments demonstrate that UniPHD produces quality results based on user-friendly prompts and achieves top-tier performance on RefHuman val and MS COCO val2017. | Referring Human Pose and Mask Estimation In the Wild | [
"Bo Miao",
"Mingtao Feng",
"Zijie Wu",
"Mohammed Bennamoun",
"Yongsheng Gao",
"Ajmal Saeed Mian"
] | NeurIPS.cc/2024/Conference | 2410.20508 | [
"https://github.com/bo-miao/refhuman"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fXDpDzHTDV | @inproceedings{
meng2024deepstack,
title={DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for {LMM}s},
author={Lingchen Meng and Jianwei Yang and Rui Tian and Xiyang Dai and Zuxuan Wu and Jianfeng Gao and Yu-Gang Jiang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fXDpDzHTDV}
} | Most large multimodal models (LMMs) are implemented by feeding visual tokens as a sequence into the first layer of a large language model (LLM).
The resulting architecture is simple but significantly increases computation and memory costs, as it has to handle a large number of additional tokens in its input layer.
This paper presents a new architecture *DeepStack* for LMMs.
Considering $N$ layers in the language and vision transformer of LMMs, we stack the visual tokens into $N$ groups and feed each group to its aligned transformer layer from bottom to top. Surprisingly, this simple method greatly enhances the power of LMMs to model interactions among visual tokens across layers but with minimal additional cost. We apply *DeepStack* to both language and vision transformer in LMMs, and
validate the effectiveness of *DeepStack* LMMs with extensive empirical results. Using the same context length, our DeepStack 7B and 13B parameters surpass their counterparts by 2.7 and 2.9 on average across 9 benchmarks, respectively. Using only one-fifth of the context length, DeepStack rivals closely to the counterparts that use the full context length. These gains are particularly pronounced on high-resolution tasks, *e.g.*, 4.2, 11.0, and 4.0 improvements on TextVQA, DocVQA, and InfoVQA compared to LLaVA-1.5-7B, respectively. We further apply *DeepStack* to vision transformer layers, which brings us a similar amount of improvements, 3.8 on average compared with LLaVA-1.5-7B. | DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for LMMs | [
"Lingchen Meng",
"Jianwei Yang",
"Rui Tian",
"Xiyang Dai",
"Zuxuan Wu",
"Jianfeng Gao",
"Yu-Gang Jiang"
] | NeurIPS.cc/2024/Conference | 2406.04334 | [
""
] | https://huggingface.co/papers/2406.04334 | 1 | 0 | 0 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=fWQhXdeuSG | @inproceedings{
li2024pretrained,
title={Pretrained Optimization Model for Zero-Shot Black Box Optimization},
author={Xiaobin Li and Kai Wu and Yujian Betterest Li and Xiaoyu Zhang and Handing Wang and Jing Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fWQhXdeuSG}
} | Zero-shot optimization involves optimizing a target task that was not seen during training, aiming to provide the optimal solution without or with minimal adjustments to the optimizer. It is crucial to ensure reliable and robust performance in various applications. Current optimizers often struggle with zero-shot optimization and require intricate hyperparameter tuning to adapt to new tasks. To address this, we propose a Pretrained Optimization Model (POM) that leverages knowledge gained from optimizing diverse tasks, offering efficient solutions to zero-shot optimization through direct application or fine-tuning with few-shot samples. Evaluation on the BBOB benchmark and two robot control tasks demonstrates that POM outperforms state-of-the-art black-box optimization methods, especially for high-dimensional tasks. Fine-tuning POM with a small number of samples and budget yields significant performance improvements. Moreover, POM demonstrates robust generalization across diverse task distributions, dimensions, population sizes, and optimization horizons. For code implementation, see https://github.com/ninja-wm/POM/. | Pretrained Optimization Model for Zero-Shot Black Box Optimization | [
"Xiaobin Li",
"Kai Wu",
"Yujian Betterest Li",
"Xiaoyu Zhang",
"Handing Wang",
"Jing Liu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=fVRCsK4EoM | @inproceedings{
liu2024prefpaint,
title={PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference},
author={Kendong Liu and Zhiyu Zhu and Chuanhao Li and Hui LIU and Huanqiang Zeng and Junhui Hou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fVRCsK4EoM}
} | In this paper, we make the first attempt to align diffusion models for image inpainting with human aesthetic standards via a reinforcement learning framework, significantly improving the quality and visual appeal of inpainted images. Specifically, instead of directly measuring the divergence with paired images, we train a reward model with the dataset we construct, consisting of nearly 51,000 images annotated with human preferences. Then, we adopt a reinforcement learning process to fine-tune the distribution of a pre-trained diffusion model for image inpainting in the direction of higher reward. Moreover, we theoretically deduce the upper bound on the error of the reward model, which illustrates the potential confidence of reward estimation throughout the reinforcement alignment process, thereby facilitating accurate regularization.
Extensive experiments on inpainting comparison and downstream tasks, such as image extension and 3D reconstruction, demonstrate the effectiveness of our approach, showing significant improvements in the alignment of inpainted images with human preference compared with state-of-the-art methods. This research not only advances the field of image inpainting but also provides a framework for incorporating human preference into the iterative refinement of generative models based on modeling reward accuracy, with broad implications for the design of visually driven AI applications. Our code and dataset are publicly available at \url{https://prefpaint.github.io}. | PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference | [
"Kendong Liu",
"Zhiyu Zhu",
"Chuanhao Li",
"Hui LIU",
"Huanqiang Zeng",
"Junhui Hou"
] | NeurIPS.cc/2024/Conference | 2410.21966 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fUBFy8tb3z | @inproceedings{
yao2024trajclip,
title={Traj{CLIP}: Pedestrian trajectory prediction method using contrastive learning and idempotent networks},
author={Pengfei Yao and Yinglong Zhu and Huikun Bi and Tianlu Mao and Zhaoqi Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fUBFy8tb3z}
} | The distribution of pedestrian trajectories is highly complex and influenced by the scene, nearby pedestrians, and subjective intentions. This complexity presents challenges for modeling and generalizing trajectory prediction. Previous methods modeled the feature space of future trajectories based on the high-dimensional feature space of historical trajectories, but this approach is suboptimal because it overlooks the similarity between historical and future trajectories. Our proposed method, TrajCLIP, utilizes contrastive learning and idempotent generative networks to address this issue. By pairing historical and future trajectories and applying contrastive learning on the encoded feature space, we enforce same-space consistency constraints. To manage complex distributions, we use idempotent loss and tightness loss to control over-expansion in the latent space. Additionally, we have developed a trajectory interpolation algorithm and synthetic trajectory data to enhance model capacity and improve generalization. Experimental results on public datasets demonstrate that TrajCLIP achieves state-of-the-art performance and excels in scene-to-scene transfer, few-shot transfer, and online learning tasks. | TrajCLIP: Pedestrian trajectory prediction method using contrastive learning and idempotent networks | [
"Pengfei Yao",
"Yinglong Zhu",
"Huikun Bi",
"Tianlu Mao",
"Zhaoqi Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=fTKcqr4xuX | @inproceedings{
zhu2024label,
title={Label Noise: Ignorance Is Bliss},
author={Yilun Zhu and Jianxin Zhang and Aditya Gangrade and Clayton Scott},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fTKcqr4xuX}
} | We establish a new theoretical framework for learning under multi-class, instance-dependent label noise.
This framework casts learning with label
noise as a form of domain adaptation, in particular, domain adaptation
under posterior drift.
We introduce the concept of \emph{relative signal strength} (RSS), a pointwise measure that quantifies the transferability from noisy to clean posterior.
Using RSS, we establish nearly matching upper and lower bounds on the excess risk.
Our theoretical findings support
the simple
\emph{Noise Ignorant Empirical Risk Minimization (NI-ERM)} principle,
which minimizes empirical risk while ignoring label noise.
Finally, we translate this theoretical insight into practice: by
using NI-ERM to fit a linear classifier on top of a self-supervised
feature extractor, we achieve state-of-the-art performance on the
CIFAR-N data challenge. | Label Noise: Ignorance Is Bliss | [
"Yilun Zhu",
"Jianxin Zhang",
"Aditya Gangrade",
"Clayton Scott"
] | NeurIPS.cc/2024/Conference | 2411.00079 | [
"https://github.com/allan-z/label_noise_ignorance"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fT1RkAgrC3 | @inproceedings{
zhan2024overparameterized,
title={Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge Distillation},
author={Yu-Liang Zhan and Zhong-Yi Lu and Hao Sun and Ze-Feng Gao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fT1RkAgrC3}
} | Increased training parameters have enabled large pre-trained models to excel in various downstream tasks. Nevertheless, the extensive computational requirements associated with these models hinder their widespread adoption within the community. We focus on Knowledge Distillation (KD), where a compact student model is trained to mimic a larger teacher model, facilitating the transfer of knowledge of large models. In contrast to much of the previous work, we scale up the parameters of the student model during training, to benefit from over-parameterization without increasing the inference latency. In particular, we propose a tensor decomposition strategy that effectively over-parameterizes the relatively small student model through an efficient and nearly lossless decomposition of its parameter matrices into higher-dimensional tensors. To ensure efficiency, we further introduce a tensor constraint loss to align the high-dimensional tensors between the student and teacher models. Comprehensive experiments validate the significant performance enhancement by our approach in various KD tasks, covering computer vision and natural language processing areas. Our code is available at https://github.com/intell-sci-comput/OPDF. | Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge Distillation | [
"Yu-Liang Zhan",
"Zhong-Yi Lu",
"Hao Sun",
"Ze-Feng Gao"
] | NeurIPS.cc/2024/Conference | 2411.06448 | [
"https://github.com/intell-sci-comput/opdf"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fPmScVB1Td | @inproceedings{
zhang2024found,
title={Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding},
author={Zhenyu Zhang and Runjin Chen and Shiwei Liu and Zhewei Yao and Olatunji Ruwase and Beidi Chen and Xiaoxia Wu and Zhangyang Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fPmScVB1Td}
} | This paper aims to overcome the ``lost-in-the-middle'' challenge of large language models (LLMs). While recent advancements have successfully enabled LLMs to perform stable language modeling with up to 4 million tokens, the persistent difficulty faced by most LLMs in identifying relevant information situated in the middle of the context has not been adequately tackled. To address this problem, this paper introduces Multi-scale Positional Encoding (Ms-PoE) which is a simple yet effective plug-and-play approach to enhance the capacity of LLMs to handle the relevant information located in the middle of the context, without fine-tuning or introducing any additional overhead. Ms-PoE leverages the position indice rescaling to relieve the long-term decay effect introduced by RoPE, while meticulously assigning distinct scaling ratios to different attention heads to preserve essential knowledge learned during the pre-training step, forming a multi-scale context fusion from short to long distance. Extensive experiments with a wide range of LLMs demonstrate the efficacy of our approach. Notably, Ms-PoE achieves an average accuracy gain of up to 3.8 on the Zero-SCROLLS benchmark over the original LLMs. Code will be made public upon acceptence. | Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding | [
"Zhenyu Zhang",
"Runjin Chen",
"Shiwei Liu",
"Zhewei Yao",
"Olatunji Ruwase",
"Beidi Chen",
"Xiaoxia Wu",
"Zhangyang Wang"
] | NeurIPS.cc/2024/Conference | 2403.04797 | [
"https://github.com/vita-group/ms-poe"
] | https://huggingface.co/papers/2403.04797 | 1 | 1 | 0 | 8 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=fPBACAbqSN | @inproceedings{
jiang2024minference,
title={{MI}nference 1.0: Accelerating Pre-filling for Long-Context {LLM}s via Dynamic Sparse Attention},
author={Huiqiang Jiang and YUCHENG LI and Chengruidong Zhang and Qianhui Wu and Xufang Luo and Surin Ahn and Zhenhua Han and Amir H. Abdi and Dongsheng Li and Chin-Yew Lin and Yuqing Yang and Lili Qiu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fPBACAbqSN}
} | The computational challenges of Large Language Model (LLM) inference remain a significant barrier to their widespread deployment, especially as prompt lengths continue to increase. Due to the quadratic complexity of the attention computation, it takes 30 minutes for an 8B LLM to process a prompt of 1M tokens (i.e., the pre-filling stage) on a single A100 GPU. Existing methods for speeding up prefilling often fail to maintain acceptable accuracy or efficiency when applied to long-context LLMs. To address this gap, we introduce MInference (Milliontokens Inference), a sparse calculation method designed to accelerate pre-filling of long-sequence processing. Specifically, we identify three unique patterns in long-context attention matrices-the A-shape, Vertical-Slash, and Block-Sparse-that can be leveraged for efficient sparse computation on GPUs. We determine the optimal pattern for each attention head offline and dynamically build sparse
indices based on the assigned pattern during inference. With the pattern and sparse indices, we perform efficient sparse attention calculations via our optimized GPU kernels to significantly reduce the latency in the pre-filling stage of longcontext LLMs. Our proposed technique can be directly applied to existing LLMs without any modifications to the pre-training setup or additional fine-tuning. By
evaluating on a wide range of downstream tasks, including InfiniteBench, RULER, PG-19, and Needle In A Haystack, and models including LLaMA-3-1M, GLM-4-1M, Yi-200K, Phi-3-128K, and Qwen2-128K, we demonstrate that MInference effectively reduces inference latency by up to 10x for pre-filling on an A100, while maintaining accuracy. Our code is available at https://aka.ms/MInference. | MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention | [
"Huiqiang Jiang",
"YUCHENG LI",
"Chengruidong Zhang",
"Qianhui Wu",
"Xufang Luo",
"Surin Ahn",
"Zhenhua Han",
"Amir H. Abdi",
"Dongsheng Li",
"Chin-Yew Lin",
"Yuqing Yang",
"Lili Qiu"
] | NeurIPS.cc/2024/Conference | 2407.02490 | [
"https://github.com/microsoft/MInference"
] | https://huggingface.co/papers/2407.02490 | 6 | 23 | 3 | 12 | [] | [] | [
"microsoft/MInference"
] | [] | [] | [
"microsoft/MInference"
] | 1 | oral |
null | https://openreview.net/forum?id=fOQunr2E0T | @inproceedings{
soulos2024compositional,
title={Compositional Generalization Across Distributional Shifts with Sparse Tree Operations},
author={Paul Soulos and Henry Conklin and Mattia Opper and Paul Smolensky and Jianfeng Gao and Roland Fernandez},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fOQunr2E0T}
} | Neural networks continue to struggle with compositional generalization, and this issue is exacerbated by a lack of massive pre-training. One successful approach for developing neural systems which exhibit human-like compositional generalization is $\textit{hybrid}$ neurosymbolic techniques. However, these techniques run into the core issues that plague symbolic approaches to AI: scalability and flexibility. The reason for this failure is that at their core, hybrid neurosymbolic models perform symbolic computation and relegate the scalable and flexible neural computation to parameterizing a symbolic system. We investigate a $\textit{unified}$ neurosymbolic system where transformations in the network can be interpreted simultaneously as both symbolic and neural computation. We extend a unified neurosymbolic architecture called the Differentiable Tree Machine in two central ways. First, we significantly increase the model’s efficiency through the use of sparse vector representations of symbolic structures. Second, we enable its application beyond the restricted set of tree2tree problems to the more general class of seq2seq problems. The improved model retains its prior generalization capabilities and, since there is a fully neural path through the network, avoids the pitfalls of other neurosymbolic techniques that elevate symbolic computation over neural computation. | Compositional Generalization Across Distributional Shifts with Sparse Tree Operations | [
"Paul Soulos",
"Henry Conklin",
"Mattia Opper",
"Paul Smolensky",
"Jianfeng Gao",
"Roland Fernandez"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=fOLNl52Q5U | @inproceedings{
ming2024simvg,
title={Sim{VG}: A Simple Framework for Visual Grounding with Decoupled Multi-modal Fusion},
author={dai ming and Lingfeng Yang and Yihao Xu and Zhenhua Feng and Wankou Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fOLNl52Q5U}
} | Visual grounding is a common vision task that involves grounding descriptive sentences to the corresponding regions of an image. Most existing methods use independent image-text encoding and apply complex hand-crafted modules or encoder-decoder architectures for modal interaction and query reasoning. However, their performance significantly drops when dealing with complex textual expressions. This is because the former paradigm only utilizes limited downstream data to fit the multi-modal feature fusion. Therefore, it is only effective when the textual expressions are relatively simple. In contrast, given the wide diversity of textual expressions and the uniqueness of downstream training data, the existing fusion module, which extracts multimodal content from a visual-linguistic context, has not been fully investigated. In this paper, we present a simple yet robust transformer-based framework, SimVG, for visual grounding. Specifically, we decouple visual-linguistic feature fusion from downstream tasks by leveraging existing multimodal pre-trained models and incorporating additional object tokens to facilitate deep integration of downstream and pre-training tasks. Furthermore, we design a dynamic weight-balance distillation method in the multi-branch synchronous learning process to enhance the representation capability of the simpler branch. This branch only consists of a lightweight MLP, which simplifies the structure and improves reasoning speed. Experiments on six widely used VG datasets, i.e., RefCOCO/+/g, ReferIt, Flickr30K, and GRefCOCO, demonstrate the superiority of SimVG. Finally, the proposed method not only achieves improvements in efficiency and convergence speed but also attains new state-of-the-art performance on these benchmarks. Codes and models are available at https://github.com/Dmmm1997/SimVG. | SimVG: A Simple Framework for Visual Grounding with Decoupled Multi-modal Fusion | [
"dai ming",
"Lingfeng Yang",
"Yihao Xu",
"Zhenhua Feng",
"Wankou Yang"
] | NeurIPS.cc/2024/Conference | 2409.17531 | [
"https://github.com/dmmm1997/simvg"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fNoleQa9RX | @inproceedings{
geng2024the,
title={The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better},
author={Scott Geng and Cheng-Yu Hsieh and Vivek Ramanujan and Matthew Wallingford and Chun-Liang Li 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=fNoleQa9RX}
} | Generative text-to-image models enable us to synthesize unlimited amounts of images in a controllable manner, spurring many recent efforts to train vision models with synthetic data. However, every synthetic image ultimately originates from the upstream data used to train the generator. Does the intermediate generator provide additional information over directly training on relevant parts of the upstream data?
Grounding this question in the setting of image classification, we compare finetuning on task-relevant, targeted synthetic data generated by Stable Diffusion---a generative model trained on the LAION-2B dataset---against finetuning on targeted real images retrieved directly from LAION-2B. We show that while synthetic data can benefit some downstream tasks, it is universally matched or outperformed by real data from the simple retrieval baseline. Our analysis suggests that this underperformance is partially due to generator artifacts and inaccurate task-relevant visual details in the synthetic images. Overall, we argue that targeted retrieval is a critical baseline to consider when training with synthetic data---a baseline that current methods do not yet surpass. We release code, data, and models at [https://github.com/scottgeng00/unmet-promise/](https://github.com/scottgeng00/unmet-promise). | The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better | [
"Scott Geng",
"Cheng-Yu Hsieh",
"Vivek Ramanujan",
"Matthew Wallingford",
"Chun-Liang Li",
"Pang Wei Koh",
"Ranjay Krishna"
] | NeurIPS.cc/2024/Conference | 2406.05184 | [
"https://github.com/scottgeng00/unmet-promise"
] | https://huggingface.co/papers/2406.05184 | 1 | 0 | 0 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=fNakQltI1N | @inproceedings{
zhang2024trajectory,
title={Trajectory Flow Matching with Applications to Clinical Time Series Modelling},
author={Xi Zhang and Yuan Pu and Yuki Kawamura and Andrew Loza and Yoshua Bengio and Dennis Shung and Alexander Tong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fNakQltI1N}
} | Modeling stochastic and irregularly sampled time series is a challenging problem found in a wide range of applications, especially in medicine. Neural stochastic differential equations (Neural SDEs) are an attractive modeling technique for this problem, which parameterize the drift and diffusion terms of an SDE with neural networks. However, current algorithms for training Neural SDEs require backpropagation through the SDE dynamics, greatly limiting their scalability and stability.
To address this, we propose **Trajectory Flow Matching** (TFM), which trains a Neural SDE in a *simulation-free* manner, bypassing backpropagation through the dynamics. TFM leverages the flow matching technique from generative modeling to model time series. In this work we first establish necessary conditions for TFM to learn time series data. Next, we present a reparameterization trick which improves training stability. Finally, we adapt TFM to the clinical time series setting, demonstrating improved performance on three clinical time series datasets both in terms of absolute performance and uncertainty prediction. | Trajectory Flow Matching with Applications to Clinical Time Series Modelling | [
"Xi Zhang",
"Yuan Pu",
"Yuki Kawamura",
"Andrew Loza",
"Yoshua Bengio",
"Dennis Shung",
"Alexander Tong"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=fMdrBucZnj | @inproceedings{
kollovieh2024expected,
title={Expected Probabilistic Hierarchies},
author={Marcel Kollovieh and Bertrand Charpentier and Daniel Z{\"u}gner and Stephan G{\"u}nnemann},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fMdrBucZnj}
} | Hierarchical clustering has usually been addressed by discrete optimization using heuristics or continuous optimization of relaxed scores for hierarchies. In this work, we propose to optimize expected scores under a probabilistic model over hierarchies. (1) We show theoretically that the global optimal values of the expected Dasgupta cost and Tree-Sampling divergence (TSD), two unsupervised metrics for hierarchical clustering, are equal to the optimal values of their discrete counterparts contrary to some relaxed scores. (2) We propose Expected Probabilistic Hierarchies (EPH), a probabilistic model to learn hierarchies in data by optimizing expected scores. EPH uses differentiable hierarchy sampling enabling end-to-end gradient descent based optimization, and an unbiased subgraph sampling approach to scale to large datasets. (3) We evaluate EPH on synthetic and real-world datasets including vector and graph datasets. EPH outperforms all other approaches quantitatively and provides meaningful hierarchies in qualitative evaluations. | Expected Probabilistic Hierarchies | [
"Marcel Kollovieh",
"Bertrand Charpentier",
"Daniel Zügner",
"Stephan Günnemann"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=fMWrTAe5Iy | @inproceedings{
zha2024rgaussian,
title={R\${\textasciicircum}2\$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction},
author={Ruyi Zha and Tao Jun Lin and Yuanhao Cai and Jiwen Cao and Yanhao Zhang and Hongdong Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fMWrTAe5Iy}
} | 3D Gaussian splatting (3DGS) has shown promising results in image rendering and surface reconstruction. However, its potential in volumetric reconstruction tasks, such as X-ray computed tomography, remains under-explored. This paper introduces R$^2$-Gaussian, the first 3DGS-based framework for sparse-view tomographic reconstruction. By carefully deriving X-ray rasterization functions, we discover a previously unknown \emph{integration bias} in the standard 3DGS formulation, which hampers accurate volume retrieval. To address this issue, we propose a novel rectification technique via refactoring the projection from 3D to 2D Gaussians. Our new method presents three key innovations: (1) introducing tailored Gaussian kernels, (2) extending rasterization to X-ray imaging, and (3) developing a CUDA-based differentiable voxelizer. Experiments on synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art approaches in accuracy and efficiency. Crucially, it delivers high-quality results in 4 minutes, which is 12$\times$ faster than NeRF-based methods and on par with traditional algorithms. | R^2-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction | [
"Ruyi Zha",
"Tao Jun Lin",
"Yuanhao Cai",
"Jiwen Cao",
"Yanhao Zhang",
"Hongdong Li"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/ruyi-zha/r2_gaussian"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=fIz8K4DJ7w | @inproceedings{
chen2024rethinking,
title={Rethinking the Diffusion Models for Missing Data Imputation: A Gradient Flow Perspective},
author={Zhichao Chen and Haoxuan Li and Fangyikang Wang and Odin Zhang and Hu Xu and Xiaoyu Jiang and Zhihuan Song and Hao Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fIz8K4DJ7w}
} | Diffusion models have demonstrated competitive performance in missing data imputation (MDI) task. However, directly applying diffusion models to MDI produces suboptimal performance due to two primary defects. First, the sample diversity promoted by diffusion models hinders the accurate inference of missing values. Second, data masking reduces observable indices for model training, obstructing imputation performance. To address these challenges, we introduce $\underline{\text{N}}$egative $\underline{\text{E}}$ntropy-regularized $\underline{\text{W}}$asserstein gradient flow for $\underline{\text{Imp}}$utation (NewImp), enhancing diffusion models for MDI from a gradient flow perspective. To handle the first defect, we incorporate a negative entropy regularization term into the cost functional to suppress diversity and improve accuracy. To handle the second defect, we demonstrate that the imputation procedure of NewImp, induced by the conditional distribution-related cost functional, can equivalently be replaced by that induced by the joint distribution, thereby naturally eliminating the need for data masking. Extensive experiments validate the effectiveness of our method. Code is available at [https://github.com/JustusvLiebig/NewImp](https://github.com/JustusvLiebig/NewImp). | Rethinking the Diffusion Models for Missing Data Imputation: A Gradient Flow Perspective | [
"Zhichao Chen",
"Haoxuan Li",
"Fangyikang Wang",
"Odin Zhang",
"Hu Xu",
"Xiaoyu Jiang",
"Zhihuan Song",
"Hao Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=fHxmoekQBh | @inproceedings{
jiang2024maven,
title={Ma{VE}n: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model},
author={Chaoya Jiang and Jia Hongrui and Haiyang Xu and Wei Ye and Mengfan Dong and Ming Yan and Ji Zhang and Fei Huang and Shikun Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fHxmoekQBh}
} | This paper presents MaVEn, an innovative Multi-granularity Visual Encoding framework designed to enhance the capabilities of Multimodal Large Language Models (MLLMs) in multi-image reasoning. Current MLLMs primarily focus on single-image visual understanding, limiting their ability to interpret and integrate information across multiple images. MaVEn addresses this limitation by combining discrete visual symbol sequences, which abstract coarse-grained semantic concepts, with traditional continuous representation sequences that model fine-grained features. This dual approach bridges the semantic gap between visual and textual data, thereby improving the model's ability to process and interpret information from multiple images effectively. Additionally, we design a dynamic reduction mechanism by for long-sequence continuous features to enhance multi-image processing efficiency. Experimental results demonstrate that MaVEn significantly enhances MLLMs' understanding in complex multi-image scenarios, while also improving performance in single-image contexts. | MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model | [
"Chaoya Jiang",
"Jia Hongrui",
"Haiyang Xu",
"Wei Ye",
"Mengfan Dong",
"Ming Yan",
"Ji Zhang",
"Fei Huang",
"Shikun Zhang"
] | NeurIPS.cc/2024/Conference | 2408.12321 | [
""
] | https://huggingface.co/papers/2408.12321 | 0 | 0 | 0 | 9 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=fHq4x2YXVv | @inproceedings{
lu2024alphapruning,
title={AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models},
author={Haiquan Lu and Yefan Zhou and Shiwei Liu and Zhangyang Wang and Michael W. Mahoney and Yaoqing Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fHq4x2YXVv}
} | Recent work on pruning large language models (LLMs) has shown that one can eliminate a large number of parameters without compromising performance, making pruning a promising strategy to reduce LLM model size. Existing LLM pruning strategies typically assign uniform pruning ratios across layers, limiting overall pruning ability; and recent work on layerwise pruning of LLMs is often based on heuristics that can easily lead to suboptimal performance. In this paper, we leverage Heavy-Tailed Self-Regularization (HT-SR) Theory, in particular the shape of empirical spectral densities (ESDs) of weight matrices, to design improved layerwise pruning ratios for LLMs. Our analysis reveals a wide variability in how well-trained, and thus relatedly how prunable, different layers of an LLM are. Based on this, we propose AlphaPruning, which uses shape metrics to allocate layerwise sparsity ratios in a more theoretically-principled manner. AlphaPruning can be used in conjunction with multiple existing LLM pruning methods. Our empirical results show that AlphaPruning prunes LLaMA-7B to 80% sparsity while maintaining reasonable perplexity, marking a first in the literature on LLMs. | AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models | [
"Haiquan Lu",
"Yefan Zhou",
"Shiwei Liu",
"Zhangyang Wang",
"Michael W. Mahoney",
"Yaoqing Yang"
] | NeurIPS.cc/2024/Conference | 2410.10912 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fG8TukiXa5 | @inproceedings{
chen2024how,
title={How Transformers Utilize Multi-Head Attention in In-Context Learning? A Case Study on Sparse Linear Regression},
author={Xingwu Chen and Lei Zhao and Difan Zou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fG8TukiXa5}
} | Despite the remarkable success of transformer-based models in various real-world tasks, their underlying mechanisms remain poorly understood. Recent studies have suggested that transformers can implement gradient descent as an in-context learner for linear regression problems and have developed various theoretical analyses accordingly. However, these works mostly focus on the expressive power of transformers by designing specific parameter constructions, lacking a comprehensive understanding of their inherent working mechanisms post-training. In this study, we consider a sparse linear regression problem and investigate how a trained multi-head transformer performs in-context learning. We experimentally discover that the utilization of multi-heads exhibits different patterns across layers: multiple heads are utilized and essential in the first layer, while usually only a single head is sufficient for subsequent layers. We provide a theoretical explanation for this observation: the first layer preprocesses the context data, and the following layers execute simple optimization steps based on the preprocessed context. Moreover, we demonstrate that such a preprocess-then-optimize algorithm can significantly outperform naive gradient descent and ridge regression algorithms. Further experimental results support our explanations. Our findings offer insights into the benefits of multi-head attention and contribute to understanding the more intricate mechanisms hidden within trained transformers. | How Transformers Utilize Multi-Head Attention in In-Context Learning? A Case Study on Sparse Linear Regression | [
"Xingwu Chen",
"Lei Zhao",
"Difan Zou"
] | NeurIPS.cc/2024/Conference | 2408.04532 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fEvUEBbEjb | @inproceedings{
chen2024tarpvp,
title={{TARP}-{VP}: Towards Evaluation of Transferred Adversarial Robustness and Privacy on Label Mapping Visual Prompting Models},
author={Zhen Chen and Yi Zhang and Fu Wang and Xingyu Zhao and Xiaowei Huang and Wenjie Ruan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fEvUEBbEjb}
} | Adversarial robustness and privacy of deep learning (DL) models are two widely studied topics in AI security. Adversarial training (AT) is
an effective approach to improve the robustness of DL models against adversarial attacks. However, while models with AT demonstrate enhanced robustness, they become more susceptible to membership inference attacks (MIAs), thus increasing the risk of privacy leakage. This indicates a negative trade-off between adversarial robustness and privacy in general deep learning models. Visual prompting is a novel model reprogramming (MR) technique used for fine-tuning pre-trained models, achieving good performance in vision tasks, especially when combined with the label mapping technique. However, the performance of label-mapping-based visual prompting (LM-VP) under adversarial attacks and MIAs lacks evaluation. In this work, we regard the MR of LM-VP as a unified entity, referred to as the LM-VP model, and take a step toward jointly evaluating the adversarial robustness and privacy of LM-VP models. Experimental results show that
the choice of pre-trained models significantly affects the white-box adversarial robustness of LM-VP, and standard AT even substantially degrades its performance. In contrast, transfer AT-trained LM-VP achieves a good trade-off between transferred adversarial robustness and privacy, a finding that has been consistently validated across various pre-trained models. Code is available at https://github.com/TrustAI/TARP-VP. | TARP-VP: Towards Evaluation of Transferred Adversarial Robustness and Privacy on Label Mapping Visual Prompting Models | [
"Zhen Chen",
"Yi Zhang",
"Fu Wang",
"Xingyu Zhao",
"Xiaowei Huang",
"Wenjie Ruan"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=fEYHZzN7kX | @inproceedings{
hu2024attractor,
title={Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective},
author={Jiaxi Hu and Yuehong HU and Wei Chen and Ming Jin and Shirui Pan and Qingsong Wen and Yuxuan Liang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fEYHZzN7kX}
} | In long-term time series forecasting (LTSF) tasks, an increasing number of works have acknowledged that discrete time series originate from continuous dynamic systems and have attempted to model their underlying dynamics. Recognizing the chaotic nature of real-world data, our model, Attraos, incorporates chaos theory into LTSF, perceiving real-world time series as low-dimensional observations from unknown high-dimensional chaotic dynamical systems. Under the concept of attractor invariance, Attraos utilizes non-parametric Phase Space Reconstruction embedding along with a novel multi-resolution dynamic memory unit to memorize historical dynamical structures, and evolves by a frequency-enhanced local evolution strategy. Detailed theoretical analysis and abundant empirical evidence consistently show that Attraos outperforms various LTSF methods on mainstream LTSF datasets and chaotic datasets with only one-twelfth of the parameters compared to PatchTST. | Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective | [
"Jiaxi Hu",
"Yuehong HU",
"Wei Chen",
"Ming Jin",
"Shirui Pan",
"Qingsong Wen",
"Yuxuan Liang"
] | NeurIPS.cc/2024/Conference | 2402.11463 | [
"https://github.com/citymind-lab/neurips24-attraos"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fE3RqiF4Nx | @inproceedings{
kapusniak2024metric,
title={Metric Flow Matching for Smooth Interpolations on the Data Manifold},
author={Kacper Kapusniak and Peter Potaptchik and Teodora Reu and Leo Zhang and Alexander Tong and Michael M. Bronstein and Joey Bose and Francesco Di Giovanni},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fE3RqiF4Nx}
} | Matching objectives underpin the success of modern generative models and rely on constructing conditional paths that transform a source distribution into a target distribution. Despite being a fundamental building block, conditional paths have been designed principally under the assumption of $\textit{Euclidean geometry}$, resulting in straight interpolations. However, this can be particularly restrictive for tasks such as trajectory inference, where straight paths might lie outside the data manifold, thus failing to capture the underlying dynamics giving rise to the observed marginals. In this paper, we propose Metric Flow Matching (MFM), a novel simulation-free framework for conditional flow matching where interpolants are approximate geodesics learned by minimizing the kinetic energy of a data-induced Riemannian metric. This way, the generative model matches vector fields on the data manifold, which corresponds to lower uncertainty and more meaningful interpolations. We prescribe general metrics to instantiate MFM, independent of the task, and test it on a suite of challenging problems including LiDAR navigation, unpaired image translation, and modeling cellular dynamics. We observe that MFM outperforms the Euclidean baselines, particularly achieving SOTA on single-cell trajectory prediction. | Metric Flow Matching for Smooth Interpolations on the Data Manifold | [
"Kacper Kapusniak",
"Peter Potaptchik",
"Teodora Reu",
"Leo Zhang",
"Alexander Tong",
"Michael M. Bronstein",
"Joey Bose",
"Francesco Di Giovanni"
] | NeurIPS.cc/2024/Conference | 2405.14780 | [
"https://github.com/kksniak/metric-flow-matching"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fDiZJ7mmOV | @inproceedings{
galashov2024nonstationary,
title={Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset},
author={Alexandre Galashov and Michalis Titsias and Andr{\'a}s Gy{\"o}rgy and Clare Lyle and Razvan Pascanu and Yee Whye Teh and Maneesh Sahani},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fDiZJ7mmOV}
} | Neural networks are traditionally trained under the assumption that data come from a stationary distribution. However, settings which violate this assumption are becoming more popular; examples include supervised learning under distributional shifts, reinforcement learning, continual learning and non-stationary contextual bandits. In this work we introduce a novel learning approach that automatically models and adapts to non-stationarity, via an Ornstein-Uhlenbeck process with an adaptive drift parameter. The adaptive drift tends to draw the parameters towards the initialisation distribution, so the approach can be understood as a form of soft parameter reset. We show empirically that our approach performs well in non-stationary supervised and off-policy reinforcement learning settings. | Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset | [
"Alexandre Galashov",
"Michalis Titsias",
"András György",
"Clare Lyle",
"Razvan Pascanu",
"Yee Whye Teh",
"Maneesh Sahani"
] | NeurIPS.cc/2024/Conference | 2411.04034 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fC2SV2sQ8J | @inproceedings{
li2024lakd,
title={La{KD}: Length-agnostic Knowledge Distillation for Trajectory Prediction with Any Length Observations},
author={Yuhang Li and Changsheng Li and Ruilin Lv and Rongqing Li and Ye Yuan and Guoren Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fC2SV2sQ8J}
} | Trajectory prediction is a crucial technology to help systems avoid traffic accidents, ensuring safe autonomous driving. Previous methods typically use a fixed-length and sufficiently long trajectory of an agent as observations to predict its future trajectory. However, in real-world scenarios, we often lack the time to gather enough trajectory points before making predictions, e.g., when a car suddenly appears due to an obstruction, the system must make immediate predictions to prevent a collision. This poses a new challenge for trajectory prediction systems, requiring them to be capable of making accurate predictions based on observed trajectories of arbitrary lengths, leading to the failure of existing methods. In this paper, we propose a Length-agnostic Knowledge Distillation framework, named LaKD, which can make accurate trajectory predictions, regardless of the length of observed data. Specifically, considering the fact that long trajectories, containing richer temporal information but potentially additional interference, may perform better or worse than short trajectories, we devise a dynamic length-agnostic knowledge distillation mechanism for exchanging information among trajectories of arbitrary lengths, dynamically determining the transfer direction based on prediction performance. In contrast to traditional knowledge distillation, LaKD employs a unique model that simultaneously serves as both the teacher and the student, potentially causing knowledge collision during the distillation process. Therefore, we design a dynamic soft-masking mechanism, where we first calculate the importance of neuron units and then apply soft-masking to them, so as to safeguard critical units from disruption during the knowledge distillation process. In essence, LaKD is a general and principled framework that can be naturally compatible with existing trajectory prediction models of different architectures. Extensive experiments on three benchmark datasets, Argoverse 1, nuScenes and Argoverse 2, demonstrate the effectiveness of our approach. | LaKD: Length-agnostic Knowledge Distillation for Trajectory Prediction with Any Length Observations | [
"Yuhang Li",
"Changsheng Li",
"Ruilin Lv",
"Rongqing Li",
"Ye Yuan",
"Guoren Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
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