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[] | Poster | [] | Modeling and analyzing object and shape has been well studied in the past. However, manipulation of these complex tools and articulated objects remains difficult for autonomous agents. Our human hands, however, are dexterous and adaptive. We can easily adapt a manipulation skill on one object to all objects in the class and to other similar classes. Our intuition comes from that there is a close connection between manipulations and topology and articulation of objects. The possible articulation of objects indicates the types of manipulation necessary to operate the object. In this work, we aim to take a manipulation perspective to understand everyday objects and tools. We collect a multi-modal visual-tactile dataset that contains paired full-hand force pressure maps and manipulation videos. We also propose a novel method to learn a cross-modal latent manifold that allow for cross-modal prediction and discovery of latent structure in different data modalities. We conduct extensive experiments to demonstrate the effectiveness of our method. | [] | [] | Learning to Jointly Understand Visual and Tactile Signals | [
"Yichen Li",
"Yilun Du",
"Chao Liu",
"Chao Liu",
"Francis Williams",
"Michael Foshey",
"Benjamin Eckart",
"Jan Kautz",
"Joshua B. Tenenbaum",
"Antonio Torralba",
"Wojciech Matusik"
] | 18,766 | https://openreview.net/forum?id=NtQqIcSbqv |
||
[] | Poster | [] | High-fidelity 3D scene reconstruction has been substantially advanced by recent progress in neural fields. However, most existing methods train a separate network from scratch for each individual scene. This is not scalable, inefficient, and unable to yield good results given limited views. While learning-based multi-view stereo methods alleviate this issue to some extent, their multi-view setting makes it less flexible to scale up and to broad applications. Instead, we introduce training generalizable Neural Fields incorporating scene Priors (NFPs). The NFP network maps any single-view RGB-D image into signed distance and radiance values. A complete scene can be reconstructed by merging individual frames in the volumetric space WITHOUT a fusion module, which provides better flexibility. The scene priors can be trained on large-scale datasets, allowing for fast adaptation to the reconstruction of a new scene with fewer views. NFP not only demonstrates SOTA scene reconstruction performance and efficiency, but it also supports single-image novel-view synthesis, which is under-explored in neural fields. | [] | [] | 3D Reconstruction with Generalizable Neural Fields using Scene Priors | [
"Yang Fu",
"Shalini De Mello",
"Xueting Li",
"Amey Kulkarni",
"Jan Kautz",
"Xiaolong Wang",
"Sifei Liu"
] | 2309.15164 | 18,765 | https://openreview.net/forum?id=Nu7dDaVF5a |
|
[
"alaa-lab/InstructCV"
] | Poster | [
"https://github.com/AlaaLab/InstructCV"
] | Recent advances in generative diffusion models have enabled text-controlled synthesis of realistic and diverse images with impressive quality. Despite these remarkable advances, the application of text-to-image generative models in computer vision for standard visual recognition tasks remains limited. The current de facto approach for these tasks is to design model architectures and loss functions that are tailored to the task at hand. In this paper, we develop a unified language interface for computer vision tasks that abstracts away task specific design choices and enables task execution by following natural language instructions. Our approach involves casting multiple computer vision tasks as text-to-image generation problems. Here, the text represents an instruction describing the task, and the resulting image is a visually-encoded task output. To train our model, we pool commonly-used computer vision datasets covering a range of tasks, including segmentation, object detection, depth estimation, and classification. We then use a large language model to paraphrase prompt templates that convey the specific tasks to be conducted on each image, and through this process, we create a multi-modal and multi-task training dataset comprising input and output images along with annotated instructions. Following the InstructPix2Pix architecture, we apply instruction-tuning to a text-to-image diffusion model using our constructed dataset, steering its functionality from a generative model to an instruction-guided multi-task vision learner. Experiments demonstrate that our model, dubbed InstructCV, performs competitively compared to other generalist and task-specific vision models. Moreover, it exhibits compelling generalization capabilities to unseen data, categories, and user instructions. | [
"alaa-lab/InstructCV"
] | [] | InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists | [
"Yulu Gan",
"Sungwoo Park",
"Alexander Marcel Schubert",
"Anthony Philippakis",
"Ahmed Alaa"
] | 2310.00390 | 18,764 | https://openreview.net/forum?id=Nu9mOSq7eH |
|
[] | Poster | [] | Popular machine learning approaches forgo second-order information due to the difficulty of computing curvature in high dimensions.We present FOSI, a novel meta-algorithm that improves the performance of any base first-order optimizer by efficiently incorporating second-order information during the optimization process.In each iteration, FOSI implicitly splits the function into two quadratic functions defined on orthogonal subspaces, then uses a second-order method to minimize the first, and the base optimizer to minimize the other.We formally analyze FOSI's convergence and the conditions under which it improves a base optimizer.Our empirical evaluation demonstrates that FOSI improves the convergence rate and optimization time of first-order methods such as Heavy-Ball and Adam, and outperforms second-order methods (K-FAC and L-BFGS). | [] | [] | FOSI: Hybrid First and Second Order Optimization | [
"Hadar Sivan",
"Moshe Gabel",
"Assaf Schuster"
] | 2302.08484 | 18,763 | https://openreview.net/forum?id=NvbeD9Ttkx |
|
[] | Poster | [] | Generative pre-trained models have demonstrated remarkable effectiveness in language and vision domains by learning useful representations. In this paper, we extend the scope of this effectiveness by showing that visual robot manipulation can significantly benefit from large-scale video generative pre-training. We introduce GR-1, a straightforward GPT-style model designed for multi-task language-conditioned visual robot manipulation. GR-1 takes as inputs a language instruction, a sequence of observation images, and a sequence of robot states. It predicts robot actions as well as future images in an end-to-end manner. Thanks to a flexible design, GR-1 can be seamlessly finetuned on robot data after pre-trained on a large-scale video dataset. We perform extensive experiments on the challenging CALVIN benchmark and a real robot. On CALVIN benchmark, our method outperforms state-of-the-art baseline methods and improves the success rate from 88.9% to 94.9%. When trained on 10% data of the full dataset, GR-1 achieves a success rate of 77.8%, while the best baseline method achieves 66.8%. In the zero-shot generalization setting, GR-1 improves the success rate from 53.3% to 85.4%. In real robot experiments, GR-1 also outperforms the comparing baseline method. We provide inaugural evidence that a unified GPT-style transformer, augmented with large-scale video generative pre-training, exhibits remarkable generalization to multi-task visual robot manipulation. Code will be made available. | [] | [] | Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation | [
"Hongtao Wu",
"Ya Jing",
"Chilam Cheang",
"Guangzeng Chen",
"Jiafeng Xu",
"Xinghang Li",
"Minghuan Liu",
"Hang Li",
"Tao Kong"
] | 2312.13139 | 18,762 | https://openreview.net/forum?id=NxoFmGgWC9 |
|
[] | Poster | [] | We introduce MAGNeT, a masked generative sequence modeling method that operates directly over several streams of discrete audio representation, i.e., tokens. Unlike prior work, MAGNeT is comprised of a single-stage, non-autoregressive transformer encoder. During training, we predict spans of masked tokens obtained from the masking scheduler, while during inference we gradually construct the output sequence using several decoding steps. To further enhance the quality of the generated audio, we introduce a novel model rescorer method. In which, we leverage an external pre-trained model to rescore and rank predictions from MAGNeT which will be then used for later decoding steps. Lastly, we explore a hybrid version of MAGNeT, in which we fuse between autoregressive and non-autoregressive models to generate the first few seconds in an autoregressive manner while the rest of the sequence is being decoded in parallel. We demonstrate the efficiency of MAGNeT over the task of text-to-music generation and conduct extensive empirical evaluation, considering both automatic and human studies. We show the proposed approach is comparable to the evaluated baselines while being significantly faster (x7 faster than the autoregressive baseline). Through ablation studies and analysis, we shed light on the importance of each of the components comprising MAGNeT, together with pointing to the trade-offs between autoregressive and non-autoregressive considering latency, throughput, and generation quality. Samples are available as part of the supplemental material. | [] | [] | Masked Audio Generation using a Single Non-Autoregressive Transformer | [
"Alon Ziv",
"Itai Gat",
"Gael Le Lan",
"Tal Remez",
"Felix Kreuk",
"Jade Copet",
"Alexandre Défossez",
"Gabriel Synnaeve",
"Yossi Adi"
] | 2401.04577 | 18,760 | https://openreview.net/forum?id=Ny8NiVfi95 |
|
[] | Poster | [] | While generative diffusion models excel in producing high-quality images, they can also be misused to mimic authorized images, posing a significant threat to AI systems. Efforts have been made to add calibrated perturbations to protect images from diffusion-based mimicry pipelines. However, most of the existing methods are too ineffective and even impractical to be used by individual users due to their high computation and memory requirements. In this work, we present novel findings on attacking latent diffusion models (LDM) and propose new plug-and-play strategies for more effective protection. In particular, we explore the bottleneck in attacking an LDM, discovering that the encoder module rather than the denoiser module is the vulnerable point. Based on this insight, we present our strategy using Score Distillation Sampling (SDS) to double the speed of protection and reduce memory occupation by half without compromising its strength. Additionally, we provide a robust protection strategy by counterintuitively minimizing the semantic loss, which can assist in generating more natural perturbations. Finally, we conduct extensive experiments to substantiate our findings and comprehensively evaluate our newly proposed strategies. We hope our insights and protective measures can contribute to better defense against malicious diffusion-based mimicry, advancing the development of secure AI systems. | [] | [] | Toward effective protection against diffusion-based mimicry through score distillation | [
"Haotian Xue",
"Chumeng Liang",
"Xiaoyu Wu",
"Yongxin Chen"
] | 2311.12832 | 18,759 | https://openreview.net/forum?id=NzxCMe88HX |
|
[] | Poster | [] | Recent text-to-3D generation methods achieve impressive 3D content creation capacity thanks to the advances in image diffusion models and optimizing strategies. However, current methods struggle to generate correct 3D content for a complex prompt in semantics, i.e., a prompt describing multiple interacted objects binding with different attributes. In this work, we propose a general framework named Progressive3D, which decomposes the entire generation into a series of locally progressive editing steps to create precise 3D content for complex prompts, and we constrain the content change to only occur in regions determined by user-defined region prompts in each editing step. Furthermore, we propose an overlapped semantic component suppression technique to encourage the optimization process to focus more on the semantic differences between prompts. Extensive experiments demonstrate that the proposed Progressive3D framework generates precise 3D content for prompts with complex semantics through progressive editing steps and is general for various text-to-3D methods driven by different 3D representations. | [] | [] | Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts | [
"Xinhua Cheng",
"Tianyu Yang",
"Jianan Wang",
"Yu Li",
"Lei Zhang",
"Jian Zhang",
"Li Yuan"
] | 2310.11784 | 18,758 | https://openreview.net/forum?id=O072Rc8uUy |
|
[] | Poster | [] | Vector drawings are innately interactive as they preserve creational cues. Despitethis desirable property they remain relatively under explored due to the difficultiesin modeling complex vector drawings. This is in part due to the primarily _sequential and auto-regressive nature_ of existing approaches failing to scale beyond simpledrawings. In this paper, we define generative models over _highly complex_ vectordrawings by first representing them as “stroke-clouds” – _sets_ of arbitrary cardinality comprised of semantically meaningful strokes. The dimensionality of thestrokes is a design choice that allows the model to adapt to a range of complexities.We learn to encode these _set of strokes_ into compact latent codes by a probabilisticreconstruction procedure backed by _De-Finetti’s Theorem of Exchangability_. Theparametric generative model is then defined over the latent vectors of the encodedstroke-clouds. The resulting “Latent stroke-cloud generator (LSG)” thus capturesthe distribution of complex vector drawings on an implicit _set space_. We demonstrate the efficacy of our model on complex drawings (a newly created Animeline-art dataset) through a rangeof generative tasks. | [] | [] | Modelling complex vector drawings with stroke-clouds | [
"Alexander Ashcroft",
"Ayan Das",
"Yulia Gryaditskaya",
"Zhiyu Qu",
"Yi-Zhe Song"
] | 18,757 | https://openreview.net/forum?id=O2jyuo89CK |
||
[] | Poster | [] | Untrustworthy users can misuse image generators to synthesize high-quality deepfakes and engage in online spam or disinformation campaigns. Watermarking deters misuse by marking generated content with a hidden message, enabling its detection using a secret watermarking key. A core security property of watermarking is robustness, which states that an attacker can only evade detection by substantially degrading image quality. Assessing robustness requires designing an adaptive attack for the specific watermarking algorithm. A challenge when evaluating watermarking algorithms and their (adaptive) attacks is to determine whether an adaptive attack is optimal, i.e., it is the best possible attack. We solve this problem by defining an objective function and then approach adaptive attacks as an optimization problem. The core idea of our adaptive attacks is to replicate secret watermarking keys locally by creating surrogate keys that are differentiable and can be used to optimize the attack's parameters. We demonstrate for Stable Diffusion models that such an attacker can break all five surveyed watermarking methods at negligible degradation in image quality. These findings emphasize the need for more rigorous robustness testing against adaptive, learnable attackers. | [] | [] | Leveraging Optimization for Adaptive Attacks on Image Watermarks | [
"Nils Lukas",
"Abdulrahman Diaa",
"Lucas Fenaux",
"Florian Kerschbaum"
] | 2309.16952 | 18,755 | https://openreview.net/forum?id=O9PArxKLe1 |
|
[] | Poster | [] | Multivariate time series forecasting plays an important role in various applications ranging from meteorology study, traffic management to economics planning. In the past decades, many efforts have been made toward accurate and reliable forecasting by exploring both temporal dynamics and spatial correlation. Especially, the development of Transformer-based methods has significantly enhanced long-term forecasting accuracy in very recent years. The existing forecasting methods often assume intact input data, however, in practice the time series data is often partially observed due to device malfunction or costly data acquisition, which can seriously impede the performance of the existing approaches. A naive employment of imputation methods unavoidably involves error accumulation and leads to suboptimal solutions. Motivated by this, we propose a Biased Temporal Convolution Graph Network that jointly captures the temporal dependencies and spatial structure. In particular, we inject bias into the two carefully developed modules---the Multi-Scale Instance PartialTCN and Biased GCN---to account for missing patterns. The experimental results show that our proposed model is able to achieve up to $11$\% improvements over the existing methods on five real-world benchmark datasets. Code is available at this repository: https://anonymous.4open.science/r/BiaTCGNet-1F80/. | [] | [] | Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values | [
"Xiaodan Chen",
"Xiucheng Li",
"Bo Liu",
"Zhijun Li"
] | 18,754 | https://openreview.net/forum?id=O9nZCwdGcG |
||
[] | Poster | [] | Faithfully summarizing the knowledge encoded by a deep neural network (DNN) into a few symbolic primitive patterns without losing much information represents a core challenge in explainable AI. To this end, Ren et al. (2023) have derived a series of theorems to prove that the inference score of a DNN can be explained as a small set of interactions between input variables. However, the lack of generalization power makes it still hard to consider such interactions as faithful primitive patterns encoded by the DNN. Therefore, given different DNNs trained for the same task, we develop a new method to extract interactions that are shared by these DNNs. Experiments show that the extracted interactions can better reflect common knowledge shared by different DNNs. | [] | [] | Defining and extracting generalizable interaction primitives from DNNs | [
"Lu Chen",
"Siyu Lou",
"Benhao Huang",
"Quanshi Zhang"
] | 2401.16318 | 18,753 | https://openreview.net/forum?id=OCqyFVFNeF |
|
[] | Poster | [] | Relational learning has gained significant attention, led by the expressiveness of Graph Neural Networks (GNNs) on graph data. While the inherent biases in common graph data are involved in GNN training, it poses a serious challenge to constraining the GNN output perturbations induced by input biases, thereby safeguarding fairness during training. The Lipschitz constant, a technique from robust statistics, can limit the maximum changes in the output concerning the input, taking into account associated irrelevant biased factors. It is an efficient and provable method to examine the output stability of machine learning models without incurring additional computational costs. Recently, its use in controlling the stability of Euclidean neural networks, the calculation of the precise Lipschitz constant remains elusive for non-Euclidean neural networks like GNNs, especially within fairness contexts. However, no existing research has investigated Lipschitz constants to shed light on stabilizing the GNN outputs, especially when working on graph data with implicit biases. To narrow this gap, we begin with the general GNNs operating on an attributed graph, and formulate a Lipschitz constant to limit the changes in the output regarding biases associated with the input. Additionally, we theoretically analyze how the Lipschitz constant of a GNN model could constrain the output perturbations induced by biases learned from data for fairness training. We experimentally validate the Lipschitz constant's effectiveness in limiting biases of the model output. Finally, from a training dynamics perspective, we demonstrate why the theoretical Lipschitz constant can effectively guide the GNN training to better trade-off between accuracy and fairness. | [] | [] | Aligning Relational Learning with Lipschitz Fairness | [
"Yaning Jia",
"Chunhui Zhang",
"Soroush Vosoughi"
] | 18,752 | https://openreview.net/forum?id=ODSgo2m8aE |
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[] | Spotlight Poster | [
"https://github.com/MC-E/DragonDiffusion"
] | Despite the ability of existing large-scale text-to-image (T2I) diffusion models to generate high-quality images from detailed textual descriptions, they often lack the ability to precisely edit the generated or real images. In this paper, we propose a novel image editing method, DragonDiffusion, enabling Drag-style manipulation on Diffusion models. Specifically, we treat image editing as the change of feature correspondence in a pre-trained diffusion model. By leveraging feature correspondence, we develop energy functions that align with the editing target, transforming image editing operations into gradient guidance. Based on this guidance approach, we also construct multi-scale guidance that considers both semantic and geometric alignment. Furthermore, we incorporate a visual cross-attention strategy based on a memory bank design to ensure consistency between the edited result and original image. Benefiting from these efficient designs, all content editing and consistency operations come from the feature correspondence without extra model fine-tuning or additional modules. Extensive experiments demonstrate that our method has promising performance on various image editing tasks, including editing within a single image (e.g., object moving, resizing, and content dragging) and across images (e.g., appearance replacing and object pasting). | [] | [] | DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models | [
"Chong Mou",
"Xintao Wang",
"Jiechong Song",
"Ying Shan",
"Jian Zhang"
] | 2307.02421 | 18,751 | https://openreview.net/forum?id=OEL4FJMg1b |
|
[] | Poster | [] | Assigning importance weights to adversarial data has achieved great success in training adversarially robust networks under limited model capacity. However, existing instance-reweighted adversarial training (AT) methods heavily depend on heuristics and/or geometric interpretations to determine those importance weights, making these algorithms lack rigorous theoretical justification/guarantee. Moreover, recent research has shown that adversarial training suffers from a severe non-uniform robust performance across the training distribution, e.g., data points belonging to some classes can be much more vulnerable to adversarial attacks than others. To address both issues, in this paper, we propose a novel doubly-robust instance reweighted AT framework, which allows to obtain the importance weights via exploring distributionally robust optimization (DRO) techniques, and at the same time boosts the robustness on the most vulnerable examples. In particular, our importance weights are obtained by optimizing the KL-divergence regularized loss function, which allows us to devise new algorithms with a theoretical convergence guarantee. Experiments on standard classification datasets demonstrate that our proposed approach outperforms related state-of-the-art baseline methods in terms of average robust performance, and at the same time improves the robustness against attacks on the weakest data points. Codes can be found in the Supplement. | [] | [] | Doubly Robust Instance-Reweighted Adversarial Training | [
"Daouda Sow",
"Sen Lin",
"Zhangyang Wang",
"Yingbin Liang"
] | 2308.00311 | 18,750 | https://openreview.net/forum?id=OF5x1dzWSS |
|
[] | Spotlight Poster | [] | Collecting large amounts of real-world interaction data to train general robotic policies is often prohibitively expensive, thus motivating the use of simulation data. However, existing methods for data generation have generally focused on scene-level diversity (e.g., object instances and poses) rather than task-level diversity, due to the human effort required to come up with and verify novel tasks. This has made it challenging for policies trained on simulation data to demonstrate significant task-level generalization. In this paper, we propose to automatically generate rich simulation environments and expert demonstrations by exploiting a large language models' (LLM) grounding and coding ability. Our approach, dubbed GenSim, has two modes: goal-directed generation, wherein a target task is given to the LLM and the LLM proposes a task curriculum to solve the target task, and exploratory generation, wherein the LLM bootstraps from previous tasks and iteratively proposes novel tasks that would be helpful in solving more complex tasks. We use GPT4 to expand the existing benchmark by ten times to over 100 tasks, on which we conduct supervised finetuning and evaluate several LLMs including finetuned GPTs and Code Llama on code generation for robotic simulation tasks. Furthermore, we observe that LLMs-generated simulation programs can enhance task-level generalization significantly when used for multitask policy training. We further find that with minimal sim-to-real adaptation, the multitask policies pretrained on GPT4-generated simulation tasks exhibit stronger transfer to unseen long-horizon tasks in the real world and outperform baselines by 25%. See our project website (https://gen-sim.github.io) and demo (https://huggingface.co/spaces/Gen-Sim/Gen-Sim) for visualizations and open-source models and datasets. | [] | [] | GenSim: Generating Robotic Simulation Tasks via Large Language Models | [
"Lirui Wang",
"Yiyang Ling",
"Zhecheng Yuan",
"Mohit Shridhar",
"Chen Bao",
"Yuzhe Qin",
"Bailin Wang",
"Huazhe Xu",
"Xiaolong Wang"
] | 2310.01361 | 18,747 | https://openreview.net/forum?id=OI3RoHoWAN |
|
[] | Poster | [] | We tackle the problem of sampling from intractable high-dimensional density functions, a fundamental task that often appears in machine learning and statistics. We extend recent sampling-based approaches that leverage controlled stochastic processes to model approximate samples from these target densities. The main drawback of these approaches is that the training objective requires full trajectories to compute, resulting in sluggish credit assignment issues due to use of entire trajectories and a weak learning signal.In this work, we present Diffusion Generative Flow Samplers (DGFS), a sampling-based framework where the learning process can be tractably broken down into short partial trajectory segments, via parameterizing an additional ``flow function''.Our method takes inspiration from the theory developed for generative flow networks (GFlowNets), allowing us to make use of intermediate learning signals and benefit from off-policy exploration capabilities.Through a variety of challenging experiments, we demonstrate that DGFS results in more accurate estimates of the normalization constant than closely-related prior methods. | [] | [] | Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization | [
"Dinghuai Zhang",
"Ricky T. Q. Chen",
"Cheng-Hao Liu",
"Aaron Courville",
"Yoshua Bengio"
] | 2310.02679 | 18,746 | https://openreview.net/forum?id=OIsahq1UYC |
|
[] | Poster | [] | MAP IT visualizes representations by taking a fundamentally different approach to dimensionality reduction. MAP IT aligns distributions over discrete marginal probabilities in the input space versus the target space, thus capturing information in local regions, as opposed to current methods which align based on individual probabilities between pairs of data points (states) only. The MAP IT theory reveals that alignment based on a projective divergence avoids normalization of weights (to obtain true probabilities) entirely, and further reveals a dual viewpoint via continuous densities and kernel smoothing. MAP IT is shown to produce visualizations which capture class structure better than the current state of the art while being inherently scalable. | [] | [] | MAP IT to Visualize Representations | [
"Robert Jenssen"
] | 18,745 | https://openreview.net/forum?id=OKf6JtXtoy |
||
[] | Poster | [] | Self-supervised pre-training of language models usually consists in predicting probability distributions over extensive token vocabularies. In this study, we propose an innovative method that shifts away from probability prediction and instead focuses on reconstructing input embeddings in a contrastive fashion via Constrastive Weight Tying (CWT). We apply this approach to pretrain Headless Language Models in both monolingual and multilingual contexts. Our method offers practical advantages, substantially reducing training computational requirements by up to 20 times, while simultaneously enhancing downstream performance and data efficiency. We observe a significant +1.6 GLUE score increase and a notable +2.7 LAMBADA accuracy improvement compared to classical LMs within similar compute budgets. | [] | [] | Headless Language Models: Learning without Predicting with Contrastive Weight Tying | [
"Nathan Godey",
"Éric Villemonte de la Clergerie",
"Benoît Sagot"
] | 2309.08351 | 18,744 | https://openreview.net/forum?id=ONPECq0Rk7 |
|
[] | Poster | [] | Bayesian optimization (BO) is a powerful approach for optimizing complex and expensive-to-evaluate black-box functions. Its importance is underscored in many applications, notably including hyperparameter tuning, but its efficacy depends on efficiently balancing exploration and exploitation. While there has been substantial progress in BO methods, striking this balance still remains a delicate process. In this light, we present $\texttt{LLAMBO}$, a novel approach that integrates the capabilities of large language models (LLM) within BO. At a high level, we frame the BO problem in natural language terms, enabling LLMs to iteratively propose promising solutions conditioned on historical evaluations. More specifically, we explore how combining contextual understanding, few-shot learning proficiency, and domain knowledge of LLMs can enhance various components of model-based BO. Our findings illustrate that $\texttt{LLAMBO}$ is effective at zero-shot warmstarting, and improves surrogate modeling and candidate sampling, especially in the early stages of search when observations are sparse. Our approach is performed in context and does not require LLM finetuning. Additionally, it is modular by design, allowing individual components to be integrated into existing BO frameworks, or function cohesively as an end-to-end method. We empirically validate $\texttt{LLAMBO}$'s efficacy on the problem of hyperparameter tuning, highlighting strong empirical performance across a range of diverse benchmarks, proprietary, and synthetic tasks. | [] | [] | Large Language Models to Enhance Bayesian Optimization | [
"Tennison Liu",
"Nicolás Astorga",
"Nabeel Seedat",
"Mihaela van der Schaar"
] | 2402.03921 | 18,743 | https://openreview.net/forum?id=OOxotBmGol |
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[] | Poster | [] | 3D visual grounding is the ability to localize objects in 3D scenes conditioned onan input utterance. Most existing methods devote the referring head to localize thereferred object directly. However, this approach will fail in complex scenarios andnot illustrate how and why the network reaches the final decision. In this paper,we address this question “Can we design an interpretable 3D visual groundingframework that has the potential to mimic the human perception system?”. To thisend, we formulate the 3D visual grounding problem as a sequence-to-sequence(Seq2Seq) task by first predicting a chain of anchors and then utilizing them to pre-dict the final target. Following the chain of thoughts approach enables us to decom-pose the referring task into interpretable intermediate steps, which in turn, booststhe performance and makes our framework extremely data-efficient. Interpretabil-ity not only improves the overall performance but also helps us identify failurecases. Moreover, our proposed framework can be easily integrated into any existingarchitecture. We validate our approach through comprehensive experiments on theNr3D and Sr3D benchmarks and show consistent performance gains compared toexisting methods without requiring any manually annotated data. Furthermore, ourproposed framework, dubbed CoT3DRef, is significantly data-efficient, whereaswhen trained only on 10% of the data, we match the SOTA performance that trainedon the entire data. The code is available at https://cot3dref.github.io/. | [] | [] | CoT3DRef: Chain-of-Thoughts Data-Efficient 3D Visual Grounding | [
"Eslam Mohamed BAKR",
"Mohamed Ayman Mohamed",
"Mahmoud Ahmed",
"Habib Slim",
"Mohamed Elhoseiny"
] | 2310.06214 | 18,742 | https://openreview.net/forum?id=ORUiqcLpV6 |
|
[] | Poster | [] | We introduce $\infty$-Diff, a generative diffusion model defined in an infinite-dimensional Hilbert space that allows infinite resolution data to be modelled. By randomly sampling subsets of coordinates during training and learning to denoise the content at those coordinates, a continuous function is learned that allows sampling at arbitrary resolutions. Prior infinite-dimensional generative models use point-wise functions that require latent compression for global context. In contrast, we propose using non-local integral operators to map between Hilbert spaces, allowing spatial information aggregation; to facilitate this, we design a powerful and efficient multi-scale architecture that operates directly on raw sparse coordinates. Training on high-resolution datasets we demonstrate that high-quality diffusion models can be learned with even $8\times$ subsampling rates, enabling substantial improvements in run-time and memory requirements, achieving significantly higher sample quality as evidenced by lower FID scores, while also being able to effectively scale to higher resolutions than the training data while retaining detail. | [] | [] | $\infty$-Diff: Infinite Resolution Diffusion with Subsampled Mollified States | [
"Sam Bond-Taylor",
"Chris G. Willcocks"
] | 18,741 | https://openreview.net/forum?id=OUeIBFhyem |
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[] | Poster | [] | Equipping agents with the capacity to justify made decisions using supporting evidence represents a cornerstone of accountable decision-making. Furthermore, ensuring that justifications are in line with human expectations and societal norms is vital, especially in high-stakes situations such as healthcare. In this work, we propose the use of a debate-based reward model for reinforcement learning agents, where an outcome of a zero-sum debate game quantifies the justifiability of a decision in a particular state. This reward model is then used to train a justifiable policy, whose decisions can be more easily corroborated with supporting evidence. In the debate game, two argumentative agents take turns providing supporting evidence for two competing decisions. Given the proposed evidence, a proxy of a human judge evaluates which decision is more justified. We demonstrate the potential of our approach in learning policies for prescribing and justifying treatment decisions of septic patients. We show that shaping the reward with the feedback signal generated by the debate-based reward model yields effective policies highly favored by the judge when compared to the policy obtained solely from the environment rewards. Moreover, in terms of the overall performance and justifiability of trained policies, the debate-based feedback is comparable to the feedback obtained from an ideal judge proxy that evaluates decisions using the full information encoded in the state. This suggests that the debate game outputs key information contained in states that is most relevant for evaluating decisions, which in turn substantiates the practicality of combining our approach with human-in-the-loop evaluations. Lastly, we showcase that agents trained via multi-agent debate learn to propose evidence that is resilient to refutations and closely aligns with human preferences. | [] | [] | Reward Design for Justifiable Sequential Decision-Making | [
"Aleksa Sukovic",
"Goran Radanovic"
] | 2402.15826 | 18,740 | https://openreview.net/forum?id=OUkZXbbwQr |
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[] | Poster | [] | Knowledge distillation aims to train a compact student network using soft supervision from a larger teacher network and hard supervision from ground truths. However, determining an optimal knowledge fusion ratio that balances these supervisory signals remains challenging. Prior methods generally resort to a constant or heuristic-based fusion ratio, which often falls short of a proper balance. In this study, we introduce a novel adaptive method for learning a sample-wise knowledge fusion ratio, exploiting both the correctness of teacher and student, as well as how well the student mimics the teacher on each sample. Our method naturally leads to the \textit{intra-sample} trilateral geometric relations among the student prediction ($\mathcal{S}$), teacher prediction ($\mathcal{T}$), and ground truth ($\mathcal{G}$). To counterbalance the impact of outliers, we further extend to the \textit{inter-sample} relations, incorporating the teacher's global average prediction ($\mathcal{\bar{T}})$ for samples within the same class. A simple neural network then learns the implicit mapping from the intra- and inter-sample relations to an adaptive, sample-wise knowledge fusion ratio in a bilevel-optimization manner. Our approach provides a simple, practical, and adaptable solution for knowledge distillation that can be employed across various architectures and model sizes. Extensive experiments demonstrate consistent improvements over other loss re-weighting methods on image classification, attack detection, and click-through rate prediction. | [] | [] | Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation | [
"Chengming Hu",
"Haolun Wu",
"Xuan Li",
"Chen Ma",
"Xi Chen",
"Boyu Wang",
"Jun Yan",
"Xue Liu"
] | 2312.15112 | 18,738 | https://openreview.net/forum?id=OZitfSXpdT |
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[] | Poster | [] | Sharpness-aware minimization (SAM) has received increasing attention in computer vision since it can effectively eliminate the sharp local minima from the training trajectory and mitigate generalization degradation. However, SAM requires two sequential gradient computations during the optimization of each step: one to obtain the perturbation gradient and the other to obtain the updating gradient. Compared with the base optimizer (e.g., Adam), SAM doubles the time overhead due to the additional perturbation gradient. By dissecting the theory of SAM and observing the training gradient of the molecular graph transformer, we propose a new algorithm named GraphSAM, which reduces the training cost of SAM and improves the generalization performance of graph transformer models. There are two key factors that contribute to this result: (i) \textit{gradient approximation}: we use the updating gradient of the previous step to approximate the perturbation gradient at the intermediate steps smoothly (\textbf{increases efficiency}); (ii) \textit{loss landscape approximation}: we theoretically prove that the loss landscape of GraphSAM is limited to a small range centered on the expected loss of SAM (\textbf{guarantees generalization performance}). The extensive experiments on six datasets with different tasks demonstrate the superiority of GraphSAM, especially in optimizing the model update process. | [] | [] | Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models | [
"Yili Wang",
"Kaixiong Zhou",
"Ninghao Liu",
"Ying Wang",
"Xin Wang"
] | 18,737 | https://openreview.net/forum?id=Od39h4XQ3Y |
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[] | Poster | [] | In our era of enormous neural networks, empirical progress has been driven by the philosophy that *more is better.*Recent deep learning practice has found repeatedly that larger model size, more data, and more computation (resulting in lower training loss) optimizing to near-interpolation improves performance. In this paper, we give theoretical backing to these empirical observations by showing that these three properties hold in random feature (RF) regression, a class of models equivalent to shallow networks with only the last layer trained.Concretely, we first show that the test risk of RF regression decreases monotonically with both the number of features and samples, provided the ridge penalty is tuned optimally. In particular, this implies that infinite width RF architectures are preferable to those of any finite width. We then proceed to demonstrate that, for a large class of tasks characterized by powerlaw eigenstructure, training to near-zero training loss is *obligatory:* near-optimal performance can *only* be achieved when the training error is much smaller than the test error. Grounding our theory in real-world data, we find empirically that standard computer vision tasks with convolutional neural kernels clearly fall into this class. Taken together, our results tell a simple, testable story of the benefits of overparameterization and overfitting in random feature models. | [] | [] | More is Better: when Infinite Overparameterization is Optimal and Overfitting is Obligatory | [
"James B Simon",
"Dhruva Karkada",
"Nikhil Ghosh",
"Mikhail Belkin"
] | 2311.14646 | 18,736 | https://openreview.net/forum?id=OdpIjS0vkO |
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[] | Poster | [] | Recent works have shown that neural networks optimized by gradient-based methods can adapt to sparse or low-dimensional target functions through feature learning; an often studied target is the sparse parity function defined on the unit hypercube. However, such isotropic data setting does not capture the anisotropy and low intrinsic dimensionality exhibited in realistic datasets. In this work, we address this shortcoming by studying how gradient-based feature learning interacts with structured (anisotropic) input data: we consider the sparse parity problem on high-dimensional orthotope where the feature coordinates have varying magnitudes, and analyze the learning complexity of the mean-field Langevin dynamics (MFLD), which describes the noisy gradient descent update on two-layer neural network. We show that the statistical complexity (i.e. sample size) and computational complexity (i.e. width of the neural network) of MFLD can both be improved when prominent directions of the anisotropic input data aligns with the support of the target function. Moreover, by employing an anisotropic weight decay regularization determined by the gradient covariance, the problem can be efficiently learned by a constant-width neural network. | [] | [] | Anisotropy helps: improved statistical and computational complexity of the mean-field Langevin dynamics under structured data | [
"Atsushi Nitanda",
"Kazusato Oko",
"Taiji Suzuki",
"Denny Wu"
] | 18,733 | https://openreview.net/forum?id=Of2nEDc4s7 |
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[] | Poster | [] | One of the key challenges in deep neural network training is the substantial amount of GPU memory required to store activations obtained in the forward pass. Various Activation-Compressed Training (ACT) schemes have been proposed to mitigate this issue; however, it is challenging to adopt those approaches in recent transformer-based large language models (LLMs), which experience significant performance drops when the activations are deeply compressed during training. In this paper, we introduce ALAM, a novel ACT framework that utilizes average quantization and a lightweight sensitivity calculation scheme, enabling large memory saving in LLMs while maintaining training performance. We first demonstrate that compressing activations into their group average values minimizes the gradient variance. Employing this property, we propose Average Quantization which provides high-quality deeply compressed activations with an effective precision of less than 1 bit and improved flexibility of precision allocation. In addition, we present a cost-effective yet accurate sensitivity calculation algorithm that solely relies on the L2 norm of parameter gradients, substantially reducing memory overhead due to sensitivity calculation. In experiments, the ALAM framework significantly reduces activation memory without compromising accuracy, achieving up to a 12.5$\times$ compression rate in LLMs. | [] | [] | ALAM: Averaged Low-Precision Activation for Memory-Efficient Training of Transformer Models | [
"Sunghyeon Woo",
"Sunwoo Lee",
"Dongsuk Jeon"
] | 18,732 | https://openreview.net/forum?id=OfXqQ5TRwp |
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[] | Poster | [] | Dynamic feature selection, where we sequentially query features to make accurate predictions with a minimal budget, is a promising paradigm to reduce feature acquisition costs and provide transparency into the prediction process. The problem is challenging, however, as it requires both making predictions with arbitrary feature sets and learning a policy to identify the most valuable selections. Here, we take an information-theoretic perspective and prioritize features based on their mutual information with the response variable. The main challenge is implementing this policy, and we design a new approach that estimates the mutual information in a discriminative rather than a generative fashion. Building on our learning approach, we introduce several further improvements: allowing variable feature budgets across samples, enabling non-uniform costs between features, incorporating prior information, and exploring modern architectures to handle partial input information. We find that our method provides consistent gains over recent state-of-the-art methods across a variety of datasets. | [] | [] | Estimating Conditional Mutual Information for Dynamic Feature Selection | [
"Soham Gadgil",
"Ian Connick Covert",
"Su-In Lee"
] | 2306.03301 | 18,731 | https://openreview.net/forum?id=Oju2Qu9jvn |
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[] | Spotlight Poster | [] | Learning an accurate value function for a given policy is a critical step in solving reinforcement learning (RL) problems. So far, however, the convergence speed and sample complexity performances of most existing policy evaluation algorithms remain unsatisfactory, particularly with non-linear function approximation. This challenge motivates us to develop a new path-integrated primal-dual stochastic gradient (PILOT) method, that is able to achieve a fast convergence speed for RL policy evaluation with nonlinear function approximation. To further alleviate the periodic full gradient evaluation requirement, we further propose an enhanced method with an adaptive-batch adjustment called PILOT$^+$. The main advantages of our methods include: i) PILOT allows the use of {\em{constant}} step sizes and achieves the $\mathcal{O}(1/K)$ convergence rate to first-order stationary points of non-convex policy evaluation problems; ii) PILOT is a generic {\em{single}}-timescale algorithm that is also applicable for solving a large class of non-convex strongly-concave minimax optimization problems; iii) By adaptively adjusting the batch size via historical stochastic gradient information, PILOT$^+$ is more sample-efficient empirically without loss of theoretical convergence rate. Our extensive numerical experiments verify our theoretical findings and showcase the high efficiency of the proposed PILOT and PILOT$^+$ algorithms compared with the state-of-the-art methods. | [] | [] | PILOT: An $\mathcal{O}(1/K)$-Convergent Approach for Policy Evaluation with Nonlinear Function Approximation | [
"Zhuqing Liu",
"Xin Zhang",
"Jia Liu",
"Zhengyuan Zhu",
"Songtao Lu"
] | 18,730 | https://openreview.net/forum?id=OkHHJcMroY |
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[] | Poster | [] | Recent advancements in autonomous driving have relied on data-driven approaches, which are widely adopted but face challenges including dataset bias, overfitting, and uninterpretability. Drawing inspiration from the knowledge-driven nature of human driving, we explore the question of how to instill similar capabilities into autonomous driving systems and summarize a paradigm that integrates an interactive environment, a driver agent, as well as a memory component to address this question. Leveraging large language models (LLMs) with emergent abilities, we propose the DiLu framework, which combines a Reasoning and a Reflection module to enable the system to perform decision-making based on common-sense knowledge and evolve continuously. Extensive experiments prove DiLu's capability to accumulate experience and demonstrate a significant advantage in generalization ability over reinforcement learning-based methods.Moreover, DiLu is able to directly acquire experiences from real-world datasets which highlights its potential to be deployed on practical autonomous driving systems.To the best of our knowledge, we are the first to leverage knowledge-driven capability in decision-making for autonomous vehicles. Through the proposed DiLu framework, LLM is strengthened to apply knowledge and to reason causally in the autonomous driving domain.Project page: https://pjlab-adg.github.io/DiLu/ | [] | [] | DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models | [
"Licheng Wen",
"Daocheng Fu",
"Xin Li",
"Xinyu Cai",
"Tao MA",
"Pinlong Cai",
"Min Dou",
"Botian Shi",
"Liang He",
"Yu Qiao"
] | 2309.16292 | 18,729 | https://openreview.net/forum?id=OqTMUPuLuC |
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[] | Poster | [] | Recently, a myriad of conditional image generation and editing models have been developed to serve different downstream tasks, including text-to-image generation, text-guided image editing, subject-driven image generation, control-guided image generation, etc. However, we observe huge inconsistencies in experimental conditions: datasets, inference, and evaluation metrics -- render fair comparisons difficult. This paper proposes ImagenHub, which is a one-stop library to standardize the inference and evaluation of all the conditional image generation models. Firstly, we define seven prominent tasks and curate high-quality evaluation datasets for them. Secondly, we built a unified inference pipeline to ensure fair comparison. Thirdly, we design two human evaluation scores, i.e. Semantic Consistency and Perceptual Quality, along with comprehensive guidelines to evaluate generated images. We train expert raters to evaluate the model outputs based on the proposed metrics. Our human evaluation achieves a high inter-worker agreement of Krippendorff’s alpha on 76\% models with a value higher than 0.4. We comprehensively evaluated a total of around 30 models and observed three key takeaways: (1) the existing models’ performance is generally unsatisfying except for Text-guided Image Generation and Subject-driven Image Generation, with 74\% models achieving an overall score lower than 0.5. (2) we examined the claims from published papers and found 83\% of them hold with a few exceptions. (3) None of the existing automatic metrics has a Spearman's correlation higher than 0.2 except subject-driven image generation. Moving forward, we will continue our efforts to evaluate newly published models and update our leaderboard to keep track of the progress in conditional image generation. | [] | [] | ImagenHub: Standardizing the evaluation of conditional image generation models | [
"Max Ku",
"Tianle Li",
"Kai Zhang",
"Yujie Lu",
"Xingyu Fu",
"Wenwen Zhuang",
"Wenhu Chen"
] | 2310.01596 | 18,726 | https://openreview.net/forum?id=OuV9ZrkQlc |
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[] | Poster | [] | Deterministic policies are often preferred over stochastic ones when implemented on physical systems. They can prevent erratic and harmful behaviors while being easier to implement and interpret. However, in practice, exploration is largely performed by stochastic policies.First-order Bayesian Optimization (BO) methods offer a principled way of performing exploration using deterministic policies. This is done through a learned probabilistic model of the objective function and its gradient. Nonetheless, such approaches treat policy search as a black-box problem, and thus, neglect the reinforcement learning nature of the problem. In this work, we leverage the performance difference lemma to introduce a novel mean function for the probabilistic model. This results in augmenting BO methods with the action-value function. Hence, we call our method Augmented Bayesian Search (ABS).Interestingly, this new mean function enhances the posterior gradient with the deterministic policy gradient, effectively bridging the gap between BO and policy gradient methods. The resulting algorithm combines the convenience of the direct policy search with the scalability of reinforcement learning.We validate ABS on high-dimensional locomotion problems and demonstrate competitive performance compared to existing direct policy search schemes. | [] | [] | Augmented Bayesian Policy Search | [
"Mahdi Kallel",
"Debabrota Basu",
"Riad Akrour",
"Carlo D'Eramo"
] | 18,724 | https://openreview.net/forum?id=OvlcyABNQT |
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[] | Spotlight Poster | [] | We introduce Robust Exploration via Clustering-based Online Density Estimation (RECODE), a non-parametric method for novelty-based exploration that estimates visitation counts for clusters of states based on their similarity in a chosen embedding space. By adapting classical clustering to the nonstationary setting of Deep RL, RECODE can efficiently track state visitation counts over thousands of episodes. We further propose a novel generalization of the inverse dynamics loss, which leverages masked transformer architectures for multi-step prediction; which in conjunction with \DETOCS achieves a new state-of-the-art in a suite of challenging 3D-exploration tasks in DM-Hard-8. RECODE also sets new state-of-the-art in hard exploration Atari games, and is the first agent to reach the end screen in "Pitfall!" | [] | [] | Unlocking the Power of Representations in Long-term Novelty-based Exploration | [
"Alaa Saade",
"Steven Kapturowski",
"Daniele Calandriello",
"Charles Blundell",
"Pablo Sprechmann",
"Leopoldo Sarra",
"Oliver Groth",
"Michal Valko",
"Bilal Piot"
] | 2305.01521 | 18,723 | https://openreview.net/forum?id=OwtMhMSybu |
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[] | Spotlight Poster | [] | Advanced techniques using Neural Radiance Fields (NeRF), Signed Distance Fields (SDF), and Occupancy Fields have recently emerged as solutions for 3D indoor scene reconstruction. We introduce a novel two-phase learning approach, H2O-SDF, that discriminates between object and non-object regions within indoor environments. This method achieves a nuanced balance, carefully preserving the geometric integrity of room layouts while also capturing intricate surface details of specific objects. A cornerstone of our two-phase learning framework is the introduction of the Object Surface Field (OSF), a novel concept designed to mitigate the persistent vanishing gradient problem that has previously hindered the capture of high-frequency details in other methods. Our proposed approach is validated through several experiments that include ablation studies. | [] | [] | H2O-SDF: Two-phase Learning for 3D Indoor Reconstruction using Object Surface Fields | [
"Minyoung Park",
"Mirae Do",
"Yeon Jae Shin",
"Jaeseok Yoo",
"Jongkwang Hong",
"Joongrock Kim",
"Chul Lee"
] | 18,720 | https://openreview.net/forum?id=P1ANzoGg3W |
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[] | Spotlight Poster | [] | The $L_{2}$-regularized loss of Deep Linear Networks (DLNs) withmore than one hidden layers has multiple local minima, correspondingto matrices with different ranks. In tasks such as matrix completion,the goal is to converge to the local minimum with the smallest rankthat still fits the training data. While rank-underestimating minimacan be avoided since they do not fit the data, GD might getstuck at rank-overestimating minima. We show that with SGD, there is always a probability to jumpfrom a higher rank minimum to a lower rank one, but the probabilityof jumping back is zero. More precisely, we define a sequence of sets$B_{1}\subset B_{2}\subset\cdots\subset B_{R}$ so that $B_{r}$contains all minima of rank $r$ or less (and not more) that are absorbingfor small enough ridge parameters $\lambda$ and learning rates $\eta$:SGD has prob. 0 of leaving $B_{r}$, and from any starting point thereis a non-zero prob. for SGD to go in $B_{r}$. | [] | [] | Implicit bias of SGD in $L_2$-regularized linear DNNs: One-way jumps from high to low rank | [
"Zihan Wang",
"Arthur Jacot"
] | 18,719 | https://openreview.net/forum?id=P1aobHnjjj |
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[] | Poster | [] | We study the unique, less-well understood problem of generating sparse adversarial samples simply by observing the score-based replies to model queries. Sparse attacks aim to discover a minimum number—the $l_0$ bounded—perturbations to model inputs to craft adversarial examples and misguide model decisions. But, in contrast to query-based dense attack counterparts against black-box models, constructing sparse adversarial perturbations, even when models serve confidence score information to queries in a score-based setting, is non-trivial. Because, such an attack leads to: i) an NP-hard problem; and ii) a non-differentiable search space. We develop the BRUSLEATTACK—a new, faster (more query-efficient) algorithm formulation for the problem. We conduct extensive attack evaluations including an attack demonstration against a Machine Learning as a Service (MLaaS) offering exemplified by __Google Cloud Vision__ and robustness testing of adversarial training regimes and a recent defense against black-box attacks. The proposed attack scales to achieve state-of-the-art attack success rates and query efficiency on standard computer vision tasks such as ImageNet across different model architectures. Our artifacts and DIY attack samples are available on GitHub. Importantly, our work facilitates faster evaluation of model vulnerabilities and raises our vigilance on the safety, security and reliability of deployed systems. | [] | [] | BRUSLEATTACK: A QUERY-EFFICIENT SCORE- BASED BLACK-BOX SPARSE ADVERSARIAL ATTACK | [
"Quoc Viet Vo",
"Ehsan Abbasnejad",
"Damith Ranasinghe"
] | 18,718 | https://openreview.net/forum?id=PAfnMGXief |
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[] | Poster | [] | Training a high-performance deep neural network requires large amounts of data and computational resources. Protecting the intellectual property (IP) and commercial ownership of a deep model is challenging yet increasingly crucial. A major stream of watermarking strategies implants verifiable backdoor triggers by poisoning training samples, but these are often unrealistic due to data privacy and safety concerns and are vulnerable to minor model changes such as fine-tuning. To overcome these challenges, we propose a safe and robust backdoor-based watermark injection technique that leverages the diverse knowledge from a single out-of-distribution (OoD) image, which serves as a secret key for IP verification. The independence of training data makes it agnostic to third-party promises of IP security. We induce robustness via random perturbation of model parameters during watermark injection to defend against common watermark removal attacks, including fine-tuning, pruning, and model extraction. Our experimental results demonstrate that the proposed watermarking approach is not only time- and sample-efficient without training data, but also robust against the watermark removal attacks above. | [] | [] | Safe and Robust Watermark Injection with a Single OoD Image | [
"Shuyang Yu",
"Junyuan Hong",
"Haobo Zhang",
"Haotao Wang",
"Zhangyang Wang",
"Jiayu Zhou"
] | 2309.01786 | 18,717 | https://openreview.net/forum?id=PCm1oT8pZI |
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[] | Poster | [] | Audio-visual speech separation methods aim to integrate different modalities to generate high-quality separated speech, thereby enhancing the performance of downstream tasks such as speech recognition. Most existing state-of-the-art (SOTA) models operate in the time domain. However, their overly simplistic approach to modeling acoustic features often necessitates larger and more computationally intensive models in order to achieve SOTA performance. In this paper, we present a novel time-frequency domain audio-visual speech separation method: Recurrent Time-Frequency Separation Network (RTFS-Net), which applies its algorithms on the complex time-frequency bins yielded by the Short-Time Fourier Transform. We model and capture the time and frequency dimensions of the audio independently using a multi-layered RNN along each dimension. Furthermore, we introduce a unique attention-based fusion technique for the efficient integration of audio and visual information, and a new mask separation approach that takes advantage of the intrinsic spectral nature of the acoustic features for a clearer separation. RTFS-Net outperforms the previous SOTA method using only 10% of the parameters and 18% of the MACs. This is the first time-frequency domain audio-visual speech separation method to outperform all contemporary time-domain counterparts. | [] | [] | RTFS-Net: Recurrent Time-Frequency Modelling for Efficient Audio-Visual Speech Separation | [
"Samuel Pegg",
"Kai Li",
"Xiaolin Hu"
] | 18,716 | https://openreview.net/forum?id=PEuDO2EiDr |
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[] | Poster | [] | A remarkable ability of human beings resides in compositional reasoning, i.e., the capacity to make "infinite use of finite means". However, current large vision-language foundation models (VLMs) fall short of such compositional abilities due to their ``bag-of-words" behaviors and inability to construct words that correctly represent visual entities and the relations among the entities. To this end, we propose Compositional VLM, which can guide the LLM to explicitly compose visual entities and relationships among the text and dynamically communicate with the vision encoder and detection network to achieve vision-language communicative decoding. Specifically, we first devise a set of novel communication tokens for the LLM, for dynamic communication between the visual detection system and the language system. A communication token is generated by the LLM following a visual entity or a relation, to inform the detection network to propose regions that are relevant to the sentence generated so far. The proposed regions-of-interests (ROIs) are then fed back into the LLM for better language generation contingent on the relevant regions. The LLM is thus able to compose the visual entities and relationships through the communication tokens. The vision-to-language and language-to-vision communication are iteratively performed until the entire sentence is generated. Our framework seamlessly bridges the gap between visual perception and LLMs and outperforms previous VLMs by a large margin on compositional reasoning benchmarks (e.g., ~20% in HICO-DET mAP, ~14% in Cola top-1 accuracy, and ~3% on ARO top-1 accuracy). We also achieve state-of-the-art performances on traditional vision-language tasks such as referring expression comprehension and visual question answering. | [] | [] | Compositional VLM: Composing Visual Entities and Relationships in Large Language Models Via Communicative Decoding | [
"Junyan Li",
"Delin Chen",
"Yining Hong",
"Zhenfang Chen",
"Peihao Chen",
"Yikang Shen",
"Chuang Gan"
] | 18,715 | https://openreview.net/forum?id=PHGxChm1l5 |
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[] | Poster | [] | The Canonical Correlation Analysis (CCA) family of methods is foundational in multi-view learning.Regularised linear CCA methods can be seen to generalise Partial Least Squares (PLS) and unified with a Generalized Eigenvalue Problem (GEP) framework.However, classical algorithms for these linear methods are computationally infeasible for large-scale data.Extensions to Deep CCA show great promise, but current training procedures are slow and complicated.First we propose a novel unconstrained objective that characterizes the top subspace of GEPs.Our core contribution is a family of fast algorithms for stochastic PLS, stochastic CCA, and Deep CCA, simply obtained by applying stochastic gradient descent (SGD) to the corresponding CCA objectives.These methods show far faster convergence and recover higher correlations than the previous state-of-the-art on all standard CCA and Deep CCA benchmarks.This speed allows us to perform a first-of-its-kind PLS analysis of an extremely large biomedical dataset from the UK Biobank, with over 33,000 individuals and 500,000 variants.Finally, we not only match the performance of `CCA-family' Self-Supervised Learning (SSL) methods on CIFAR-10 and CIFAR-100 with minimal hyper-parameter tuning, but also establish the first solid theoretical links to classical CCA, laying the groundwork for future insights. | [] | [] | Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning | [
"James Chapman",
"Lennie Wells",
"Ana Lawry Aguila"
] | 2310.01012 | 18,714 | https://openreview.net/forum?id=PHLVmV88Zy |
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[] | Poster | [
"https://github.com/ant-research/EasyTemporalPointProcess"
] | Continuous-time event sequences play a vital role in real-world domains such as healthcare, finance, online shopping, social networks, and so on. To model such data, temporal point processes (TPPs) have emerged as the most natural and competitive models, making a significant impact in both academic and application communities. Despite the emergence of many powerful models in recent years, there hasn't been a central benchmark for these models and future research endeavors. This lack of standardization impedes researchers and practitioners from comparing methods and reproducing results, potentially slowing down progress in this field. In this paper, we present EasyTPP, the first central repository of research assets (e.g., data, models, evaluation programs, documentations) in the area of event sequence modeling. Our EasyTPP makes several unique contributions to this area: a unified interface of using existing datasets and adding new datasets; a wide range of evaluation programs that are easy to use and extend as well as facilitate reproducible research; implementations of popular neural TPPs, together with a rich library of modules by composing which one could quickly build complex models. All the data and implementation can be found anonymously at Github repository: https://github.com/Anonymous0006/EasyTPP. We will actively maintain this benchmark and welcome contributions from other researchers and practitioners. Our benchmark will help promote reproducible research in this field, thus accelerating research progress as well as making more significant real-world impacts. | [] | [] | EasyTPP: Towards Open Benchmarking Temporal Point Processes | [
"Siqiao Xue",
"Xiaoming Shi",
"Zhixuan Chu",
"Yan Wang",
"Hongyan Hao",
"Fan Zhou",
"Caigao JIANG",
"Chen Pan",
"James Y. Zhang",
"Qingsong Wen",
"JUN ZHOU",
"Hongyuan Mei"
] | 2307.08097 | 18,712 | https://openreview.net/forum?id=PJwAkg0z7h |
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[] | Poster | [] | In this work, we study the problem of explicit NeRF compression. Through analyzing recent explicit NeRF models, we reformulate the task of explicit NeRF compression as 3D data compression. We further introduce our NeRF compression framework, Attributed Compression of Radiance Field (ACRF), which focuses on the compression of the explicit neural 3D representation. The neural 3D structure is pruned and converted to points with features, which are further encoded using importance-guided feature encoding. Furthermore, we employ an importance-prioritized entropy model to estimate the probability distribution of transform coefficients, which are then entropy coded with an arithmetic coder using the predicted distribution. Within this framework, we present two models, ACRF and ACRF-F, to strike a balance between compression performance and encoding time budget. Our experiments, which include both synthetic and real-world datasets such as Synthetic-NeRF and Tanks&Temples, demonstrate the superior performance of our proposed algorithm. | [] | [] | ACRF: Compressing Explicit Neural Radiance Fields via Attribute Compression | [
"Guangchi Fang",
"Qingyong Hu",
"Longguang Wang",
"Yulan Guo"
] | 18,709 | https://openreview.net/forum?id=POFrdKvpea |
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[] | Poster | [] | Connecting optimal transport and variational inference, we present a principled and systematic framework for sampling and generative modelling centred around divergences on path space. Our work culminates in the development of the Controlled Monte Carlo Diffusion sampler (CMCD) for Bayesian computation, a score-based annealing technique that crucially adapts both forward and backward dynamics in a diffusion model. On the way, we clarify the relationship between the EM-algorithm and iterative proportional fitting (IPF) for Schroedinger bridges, deriving as well a regularised objective that bypasses the iterative bottleneck of standard IPF-updates. Finally, we show that CMCD has a strong foundation in the Jarzinsky and Crooks identities from statistical physics, and that it convincingly outperforms competing approaches across a wide array of experiments. | [] | [] | Transport meets Variational Inference: Controlled Monte Carlo Diffusions | [
"Francisco Vargas",
"Shreyas Padhy",
"Denis Blessing",
"Nikolas Nüsken"
] | 2307.01050 | 18,708 | https://openreview.net/forum?id=PP1rudnxiW |
|
[] | Spotlight Poster | [] | Post hoc privacy auditing techniques can be used to test the privacy guarantees of a model, but come with several limitations: (i) they can only establish lower bounds on the privacy loss, (ii) the intermediate model updates and some data must be shared with the auditor to get a better approximation of the privacy loss, and (iii) the auditor typically faces a steep computational cost to run a large number of attacks. In this paper, we propose to proactively generate a cryptographic certificate of privacy during training to forego such auditing limitations. We introduce Confidential-DPproof , a framework for Confidential Proof of Differentially Private Training, which enhances training with a certificate of the $(\varepsilon,\delta)$-DP guarantee achieved. To obtain this certificate without revealing information about the training data or model, we design a customized zero-knowledge proof protocol tailored to the requirements introduced by differentially private training, including random noise addition and privacy amplification by subsampling. In experiments on CIFAR-10, Confidential-DPproof trains a model achieving state-of-the-art $91$% test accuracy with a certified privacy guarantee of $(\varepsilon=0.55,\delta=10^{-5})$-DP in approximately 100 hours. | [] | [] | Confidential-DPproof: Confidential Proof of Differentially Private Training | [
"Ali Shahin Shamsabadi",
"Gefei Tan",
"Tudor Ioan Cebere",
"Aurélien Bellet",
"Hamed Haddadi",
"Nicolas Papernot",
"Xiao Wang",
"Adrian Weller"
] | 18,707 | https://openreview.net/forum?id=PQY2v6VtGe |
||
[] | Poster | [] | The conjugate gradient method is a crucial first-order optimization method that generally converges faster than the steepest descent method, and its computational cost is much lower than the second-order methods. However, while various types of conjugate gradient methods have been studied in Euclidean spaces and on Riemannian manifolds, there is little study for those in distributed scenarios. This paper proposes a decentralized Riemannian conjugate gradient descent (DRCGD) method that aims at minimizing a global function over the Stiefel manifold. The optimization problem is distributed among a network of agents, where each agent is associated with a local function, and the communication between agents occurs over an undirected connected graph. Since the Stiefel manifold is a non-convex set, a global function is represented as a finite sum of possibly non-convex (but smooth) local functions. The proposed method is free from expensive Riemannian geometric operations such as retractions, exponential maps, and vector transports, thereby reducing the computational complexity required by each agent. To the best of our knowledge, DRCGD is the first decentralized Riemannian conjugate gradient algorithm to achieve global convergence over the Stiefel manifold. The numerical experiments reveal that the advantages of our DRCGD. | [] | [] | Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold | [
"Jun Chen",
"Haishan Ye",
"Mengmeng Wang",
"Tianxin Huang",
"Guang Dai",
"Ivor Tsang",
"Yong Liu"
] | 2308.10547 | 18,706 | https://openreview.net/forum?id=PQbFUMKLFp |
|
[] | Poster | [] | Large language models (LLMs) fine-tuned with reinforcement learning from humanfeedback (RLHF) have been used in some of the most widely deployed AI modelsto date, such as OpenAI’s ChatGPT or Anthropic’s Claude. While there has beensignificant work developing these methods, our understanding of the benefits anddownsides of each stage in RLHF is still limited. To fill this gap, we present anextensive analysis of how each stage of the process (i.e. supervised fine-tuning(SFT), reward modelling, and RLHF) affects two key properties: out-of-distributiongeneralisation (OOD) and output diversity. OOD generalisation is crucial given thewide range of real-world scenarios in which these models are being used, whileoutput diversity refers to the model’s ability to generate varied outputs, and isimportant for a variety of use cases. We perform our analysis across two basemodels on both summarisation and instruction following tasks, the latter beinghighly relevant for current LLM use cases. We find that RLHF generalises betterthan SFT to new inputs, particularly as the distribution shift between train and testbecomes larger. However, RLHF significantly reduces output diversity compared toSFT across a variety of measures, implying a tradeoff in current LLM fine-tuningmethods between generalisation and diversity. Our results provide guidance onwhich fine-tuning method should be used depending on the application, and showthat more research is needed to improve the tradeoff between generalisation anddiversity. | [] | [] | Understanding the Effects of RLHF on LLM Generalisation and Diversity | [
"Robert Kirk",
"Ishita Mediratta",
"Christoforos Nalmpantis",
"Jelena Luketina",
"Eric Hambro",
"Edward Grefenstette",
"Roberta Raileanu"
] | 2310.06452 | 18,704 | https://openreview.net/forum?id=PXD3FAVHJT |
|
[] | Poster | [] | Open-world entity segmentation, as an emerging computer vision task, aims at segmenting entities in images without being restricted by pre-defined classes, offering impressive generalization capabilities on unseen images and concepts. Despite its promise, existing entity segmentation methods like Segment Anything Model (SAM) rely heavily on costly expert annotators. This work presents Self-supervised Open-world Hierarchical Entity Segmentation (SOHES), a novel approach that sidesteps the need for human annotations. SOHES operates in three phases: self-exploration, self-instruction, and self-correction. Given a pre-trained self-supervised representation, we produce abundant high-quality pseudo-labels through visual feature clustering. Then, we train a segmentation model on the pseudo-labels, and rectify the noises in pseudo-labels via a teacher-student mutual-learning procedure. Beyond segmenting entities, SOHES also captures their constituent parts, providing a hierarchical understanding of visual entities. Using raw images as the sole training data, our method achieves unprecedented performance in self-supervised open-world segmentation, marking a significant milestone towards high-quality open-world entity segmentation in the absence of human-annotated masks. | [] | [] | SOHES: Self-supervised Open-world Hierarchical Entity Segmentation | [
"Shengcao Cao",
"Jiuxiang Gu",
"Jason Kuen",
"Hao Tan",
"Ruiyi Zhang",
"Handong Zhao",
"Ani Nenkova",
"Liangyan Gui",
"Tong Sun",
"Yu-Xiong Wang"
] | 2404.12386 | 18,703 | https://openreview.net/forum?id=PXNrncg2DF |
|
[] | Poster | [] | The Boolean satisfiability problem (SAT) stands as a canonical NP-complete combinatorial optimization (CO) problem, with wide impact on both theoretical and industrial scenarios. In particular, the scarcity of real-world SAT instances and their usefulness for tuning SAT solvers underscore the necessity for effective and efficient ways of hard instance generation, whereas existing methods either struggle to maintain plausible hardness or suffer from limited applicability. Different from the typical construction-based methods, this paper introduces an adaptive and efficient graph interpolation approach that in place modifies the raw structure of graph-represented SAT instance by replacing it with a counterpart from another instance. Specifically, our method involves a two-stage matching and mixing pipeline. The matching aims to find a correspondence map of literal nodes from two instance graphs via learned features from a matching network; while the mixing stage involves iteratively exchanging clause pairs with the highest correspondence scores until a specified replacement ratio is achieved. We further show that under our matching-mixing framework, moderate randomness can avoid hardness degradation of SAT instances by introducing Gumbel noise. Experimental results show the superiority of the proposed method with both resemblance in structure and hardness, as well as general applicability in an efficient way. Source code will be released. | [] | [] | MixSATGEN: Learning Graph Mixing for SAT Instance Generation | [
"Xinyan Chen",
"Yang Li",
"Runzhong Wang",
"Junchi Yan"
] | 18,702 | https://openreview.net/forum?id=PXXuLvIH5r |
||
[] | Poster | [] | Recently, it has been shown that for offline deep reinforcement learning (DRL), pre-training Decision Transformer with a large language corpus can improve downstream performance (Reid et al., 2022). A natural question to ask is whether this performance gain can only be achieved with language pre-training, or can be achieved with simpler pre-training schemes which do not involve language. In this paper, we first show that language is not essential for improved performance, and indeed pre-training with synthetic IID data for a small number of updates can match the performance gains from pre-training with a large language corpus; moreover, pre-training with data generated by a one-step Markov chain can further improve the performance. Inspired by these experimental results, we then consider pre-training Conservative Q-Learning (CQL), a popular offline DRL algorithm, which is Q-learning-based and typically employs a Multi-Layer Perceptron (MLP) backbone. Surprisingly, pre-training with simple synthetic data for a small number of updates can also improve CQL, providing consistent performance improvement on D4RL Gym locomotion datasets. The results of this paper not only illustrate the importance of pre-training for offline DRL but also show that the pre-training data can be synthetic and generated with remarkably simple mechanisms. | [] | [] | Pre-training with Synthetic Data Helps Offline Reinforcement Learning | [
"Zecheng Wang",
"Che Wang",
"Zixuan Dong",
"Keith W. Ross"
] | 2310.00771 | 18,700 | https://openreview.net/forum?id=PcxQgtHGj2 |
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[] | Spotlight Poster | [] | Sample efficiency is a crucial problem in deep reinforcement learning. Recent algorithms, such as REDQ and DroQ, found a way to improve the sample efficiency by increasing the update-to-data (UTD) ratio to 20 gradient update steps on the critic per environment sample. However, this comes at the expense of a greatly increased computational cost. To reduce this computational burden, we introduce CrossQ: a lightweight algorithm that makes careful use of Batch Normalization and removes target networks to surpass the state-of-the-art in sample efficiency while maintaining a low UTD ratio of 1. Notably, CrossQ does not rely on advanced bias-reduction schemes used in current methods. CrossQ’s contributions are thus threefold: (1) state-of-the-art sample efficiency, (2) substantial reduction in computational cost compared to REDQ and DroQ, and (3) ease of implementation, requiring just a few lines of code on top of SAC. | [] | [] | CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity | [
"Aditya Bhatt",
"Daniel Palenicek",
"Boris Belousov",
"Max Argus",
"Artemij Amiranashvili",
"Thomas Brox",
"Jan Peters"
] | 1902.05605 | 18,699 | https://openreview.net/forum?id=PczQtTsTIX |
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[] | Poster | [] | The role of machine learning in computing atomic properties is expanding rapidly for a wide range of applications from healthcare to climate change. One important ingredient that has enabled this development is the creation of large and diverse molecular datasets. Given the extreme computational cost of these datasets, an important question moving forward is: Can we limit the need for exhaustive large dataset creation by pre-training a foundation style model over multiple chemical domains to generate transferable atomic representations for downstream fine-tuning tasks? Generalization across the entire molecular space is challenging due to the range and complexity of atomic interactions that exist. In this paper, we present Joint Multi-domain Pre-training (JMP), a robust supervised pre-training strategy that utilizes data from multiple chemical domains, $\sim$120 million examples in total. We demonstrate state-of-the-art results across many targets of the rMD17, QM9, MatBench, QMOF, SPICE, and MD22 datasets. Finally, we conduct ablations to study the impact of different components of JMP on downstream performance. | [] | [] | From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction | [
"Nima Shoghi",
"Adeesh Kolluru",
"John R. Kitchin",
"Zachary Ward Ulissi",
"C. Lawrence Zitnick",
"Brandon M Wood"
] | 2310.16802 | 18,696 | https://openreview.net/forum?id=PfPnugdxup |
|
[] | Spotlight Poster | [] | In human neuroimaging, multi-modal imaging techniques are frequently combined to enhance our comprehension of whole-brain dynamics and improve diagnosis in clinical practice. Modalities like electroencephalography and functional magnetic resonance imaging provide distinct views of brain dynamics due to diametral spatiotemporal sensitivities and underlying neurophysiological coupling mechanisms. These distinct views pose a considerable challenge to learning a shared representation space, especially when dealing with covariance-based data characterized by their geometric structure. To capitalize on the geometric structure, we introduce a measure called geodesic correlation, which expands traditional correlation consistency to covariance-based data on the symmetric positive definite (SPD) manifold. This measure is derived from classical canonical correlation analysis and serves to evaluate the consistency of latent representations obtained from paired views. For multi-view/-modal, self-supervised learning where one or both latent views are SPD, we propose an innovative geometric deep learning framework termed DeepGeoCCA. Its primary objective is to enhance the geodesic correlation of unlabeled, paired data, thereby generating novel representations while retaining the geometric structures. In simulations and experiments with multi-view and multi-modal human neuroimaging data, we find that DeepGeoCCA learns latent representations with high geodesic consistency for unseen data while retaining relevant information for downstream tasks. | [] | [] | Deep Geodesic Canonical Correlation Analysis for Covariance-Based Neuroimaging Data | [
"Ce Ju",
"Reinmar J Kobler",
"Liyao Tang",
"Cuntai Guan",
"Motoaki Kawanabe"
] | 18,694 | https://openreview.net/forum?id=PnR1MNen7u |
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[] | Poster | [] | Estimating time-varying causal effects from real-world data attracts growing attention due to the high cost of performing interventional experiments and the wide availability of observational data. However, counterfactual data are not accessible, and true calculation of causal effects cannot be performed at the individual level. This paper proposes a linear algebraic framework to generate synthetic counterfactual data that exactly matches pretreatment factual data. Receiving treatment at a time $T_0$ cannot cause any difference reversely to counterfactual generation at $t < T_0$. If we hold this strictness on counterfactual generation, our approach claims the first-ever counterfactual generative model to create personalized clinical trial digital twins. Moreover, using simulated ground truth counterfactual data, we show that our method greatly outperforms the most cited methods of counterfactual generation and individual treatment effect estimation. We also provide a formula that can estimate the time-varying variance of individual treatment effects, interpreted as a confidence of generated counterfactuals to true values. | [] | [] | A Linear Algebraic Framework for Counterfactual Generation | [
"Jong-Hoon Ahn",
"Akshay Vashist"
] | 18,693 | https://openreview.net/forum?id=PoDkdFQIu3 |
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[] | Poster | [] | Bilevel programs (BPs) find a wide range of applications in fields such as energy, transportation, and machine learning. As compared to BPs with continuous (linear/convex) optimization problems in both levels, the BPs with discrete decision variables have received much less attention, largely due to the ensuing computational intractability and the incapability of gradient-based algorithms for handling discrete optimization formulations. In this paper, we develop deep learning techniques to address this challenge. Specifically, we consider a BP with binary tender, wherein the upper and lower levels are linked via binary variables. We train a neural network to approximate the optimal value of the lower-level problem, as a function of the binary tender. Then, we obtain a single-level reformulation of the BP through a mixed-integer representation of the value function. Furthermore, we conduct a comparative analysis between two types of neural networks: general neural networks and the novel input supermodular neural networks, studying their representational capacities. To solve high-dimensional BPs, we introduce an enhanced sampling method to generate higher-quality samples and implement an iterative process to refine solutions. We demonstrate the performance of these approaches through extensive numerical experiments, whose lower-level problems are linear and mixed-integer programs, respectively. | [] | [] | Learning to Solve Bilevel Programs with Binary Tender | [
"Bo Zhou",
"Ruiwei Jiang",
"Siqian Shen"
] | 18,692 | https://openreview.net/forum?id=PsDFgTosqb |
||
[] | Spotlight Poster | [] | Learning feature representations of geographical space is vital for any machine learning model that integrates geolocated data, spanning application domains such as remote sensing, ecology, or epidemiology.Recent work mostly embeds coordinates using sine and cosine projections based on Double Fourier Sphere (DFS) features -- these embeddings assume a rectangular data domain even on global data, which can lead to artifacts, especially at the poles. At the same time, relatively little attention has been paid to the exact design of the neural network architectures these functional embeddings are combined with. This work proposes a novel location encoder for globally distributed geographic data that combines spherical harmonic basis functions, natively defined on spherical surfaces, with sinusoidal representation networks (SirenNets) that can be interpreted as learned Double Fourier Sphere embedding. We systematically evaluate the cross-product of positional embeddings and neural network architectures across various classification and regression benchmarks and synthetic evaluation datasets. In contrast to previous approaches that require the combination of both positional encoding and neural networks to learn meaningful representations, we show that both spherical harmonics and sinusoidal representation networks are competitive on their own but set state-of-the-art performances across tasks when combined. | [] | [] | Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks | [
"Marc Rußwurm",
"Konstantin Klemmer",
"Esther Rolf",
"Robin Zbinden",
"Devis Tuia"
] | 2310.06743 | 18,690 | https://openreview.net/forum?id=PudduufFLa |
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[] | Poster | [] | While neural networks can be approximated by linear models as their width increases, certain properties of wide neural networks cannot be captured by linear models. In this work we show that recently proposed Neural Quadratic Models can exhibit the "catapult phase" Lewkowycz et al. (2020) that arises when training such models with large learning rates. We then empirically show that the behaviour of quadratic models parallels that of neural networks in generalization, especially in the catapult phase regime. Our analysis further demonstrates that quadratic models are an effective tool for analysis of neural networks. | [] | [] | Quadratic models for understanding catapult dynamics of neural networks | [
"Libin Zhu",
"Chaoyue Liu",
"Adityanarayanan Radhakrishnan",
"Mikhail Belkin"
] | 2205.11787 | 18,689 | https://openreview.net/forum?id=PvJnX3dwsD |
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[] | Poster | [] | Modern learning frameworks often train deep neural networks with massive amounts of unlabeled data to learn representations by solving simple pretext tasks, then use the representations as foundations for downstream tasks. These networks are empirically designed; as such, they are usually not interpretable, their representations are not structured, and their designs are potentially redundant. White-box deep networks, in which each layer explicitly identifies and transforms structures in the data, present a promising alternative. However, existing white-box architectures have only been shown to work at scale in supervised settings with labeled data, such as classification. In this work, we provide the first instantiation of the white-box design paradigm that can be applied to large-scale unsupervised representation learning. We do this by exploiting a fundamental connection between diffusion, compression, and (masked) completion, deriving a deep transformer-like masked autoencoder architecture, called CRATE-MAE, in which the role of each layer is mathematically fully interpretable: they transform the data distribution to and from a structured representation. Extensive empirical evaluations confirm our analytical insights. CRATE-MAE demonstrates highly promising performance on large-scale imagery datasets while using only ~30% of the parameters compared to the standard masked autoencoder with the same model configuration. The representations learned by CRATE-MAE have explicit structure and also contain semantic meaning. | [] | [] | Masked Completion via Structured Diffusion with White-Box Transformers | [
"Druv Pai",
"Sam Buchanan",
"Ziyang Wu",
"Yaodong Yu",
"Yi Ma"
] | 2404.02446 | 18,688 | https://openreview.net/forum?id=PvyOYleymy |
|
[] | Poster | [
"https://github.com/locuslab/wanda"
] | As their size increases, Large Languages Models (LLMs) are natural candidates for network pruning methods: approaches that drop a subset of network weights while striving to preserve performance. Existing methods, however, require either retraining, which is rarely affordable for billion-scale LLMs, or solving a weight reconstruction problem reliant on second-order information, which may also becomputationally expensive. In this paper, we introduce a novel, straightforward yet effective pruning method, termed Wanda (Pruning by Weights and activations), designed to induce sparsity in pretrained LLMs. Motivated by the recent observation of emergent large magnitude features in LLMs, our approach prunes weights with the smallest magnitudes multiplied by the corresponding input activations, ona per-output basis. Notably, Wanda requires no retraining or weight update, and the pruned LLM can be used as is. We conduct a thorough evaluation of our method on LLaMA and LLaMA-2 across various language benchmarks. Wanda significantly outperforms the established baseline of magnitude pruning and performs competitively against recent methods involving intensive weight updates. | [] | [] | A Simple and Effective Pruning Approach for Large Language Models | [
"Mingjie Sun",
"Zhuang Liu",
"Anna Bair",
"J Zico Kolter"
] | 2306.11695 | 18,687 | https://openreview.net/forum?id=PxoFut3dWW |
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[] | Poster | [] | Recent advances in large language model (LLM) pretraining have led to high-quality LLMs with impressive abilities. By compressing such LLMs via quantization to 3-4 bits per parameter, they can fit into memory-limited devices such as laptops and mobile phones, enabling personalized use. Quantizing models to 3-4 bits per parameter can lead to moderate to high accuracy losses, especially for smaller models (1-10B parameters), which are suitable for edge deployment. To address this accuracy issue, we introduce the Sparse-Quantized Representation (SpQR), a new compressed format and quantization technique that enables for the first time \emph{near-lossless} compression of LLMs across model scales while reaching similar compression levels to previous methods. SpQR works by identifying and isolating \emph{outlier weights}, which cause particularly large quantization errors, and storing them in higher precision while compressing all other weights to 3-4 bits, and achieves relative accuracy losses of less than $1\%$ in perplexity for highly-accurate LLaMA and Falcon LLMs. This makes it possible to run a 33B parameter LLM on a single 24 GB consumer GPU without performance degradation at 15\% speedup, thus making powerful LLMs available to consumers without any downsides. SpQR comes with efficient algorithms for both encoding weights into its format, as well as decoding them efficiently at runtime. Specifically, we provide an efficient GPU inference algorithm for SpQR, which yields faster inference than 16-bit baselines at similar accuracy while enabling memory compression gains of more than 4x. | [] | [] | SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression | [
"Tim Dettmers",
"Ruslan A. Svirschevski",
"Vage Egiazarian",
"Denis Kuznedelev",
"Elias Frantar",
"Saleh Ashkboos",
"Alexander Borzunov",
"Torsten Hoefler",
"Dan Alistarh"
] | 2306.03078 | 18,686 | https://openreview.net/forum?id=Q1u25ahSuy |
|
[] | Poster | [
"https://github.com/BraveGroup/PointSAM-for-MixSup"
] | Label-efficient LiDAR-based 3D object detection is currently dominated by weak/semi-supervised methods. Instead of exclusively following one of them, we propose MixSup, a more practical paradigm simultaneously utilizing massive cheap coarse labels and a limited number of accurate labels for Mixed-grained Supervision. We start by observing that point clouds are usually textureless, making it hard to learn semantics. However, point clouds are geometrically rich and scale-invariant to the distances from sensors, making it relatively easy to learn the geometry of objects, such as poses and shapes. Thus, MixSup leverages massive coarse cluster-level labels to learn semantics and a few expensive box-level labels to learn accurate poses and shapes. We redesign the label assignment in mainstream detectors, which allows them seamlessly integrated into MixSup, enabling practicality and universality. We validate its effectiveness in nuScenes, Waymo Open Dataset, and KITTI, employing various detectors. MixSup achieves up to 97.31% of fully supervised performance, using cheap cluster annotations and only 10% box annotations. Furthermore, we utilize the emerging Segment Anything Model (SAM) to automatically generate massive coarse labels, further reducing the annotation burden. The code will be made publicly available. | [] | [] | MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object Detection | [
"Yuxue Yang",
"Lue Fan",
"Zhaoxiang Zhang"
] | 2401.16305 | 18,685 | https://openreview.net/forum?id=Q1vkAhdI6j |
|
[] | Poster | [] | The demonstrated code-understanding capability of LLMs raises the question of whether they can be used for automated program verification, a task that typically demands high-level abstract reasoning about program properties that is challenging for verification tools. We propose a general methodology to combine the power of LLMs and automated reasoners for automated program verification. We formally describe this methodology as a set of derivation rules and prove its soundness. We instantiate the calculus as a sound automated verification procedure, which led to practical improvements on a set of synthetic and competition benchmarks. | [] | [] | Lemur: Integrating Large Language Models in Automated Program Verification | [
"Haoze Wu",
"Clark Barrett",
"Nina Narodytska"
] | 2310.04870 | 18,684 | https://openreview.net/forum?id=Q3YaCghZNt |
|
[] | Poster | [] | In this work, we propose a Multi-Window Masked Autoencoder (MW-MAE) fitted with a novel Multi-Window Multi-Head Attention (MW-MHA) module that facilitates the modelling of local-global interactions in every decoder transformer block through attention heads of several distinct local and global windows. Empirical results on ten downstream audio tasks show that MW-MAEs consistently outperform standard MAEs in overall performance and learn better general-purpose audio representations, along with demonstrating considerably better scaling characteristics. Investigating attention distances and entropies reveals that MW-MAE encoders learn heads with broader local and global attention. Analyzing attention head feature representations through Projection Weighted Canonical Correlation Analysis (PWCCA) shows that attention heads with the same window sizes across the decoder layers of the MW-MAE learn correlated feature representations which enables each block to independently capture local and global information, leading to a decoupled decoder feature hierarchy. | [] | [] | Masked Autoencoders with Multi-Window Local-Global Attention Are Better Audio Learners | [
"Sarthak Yadav",
"Sergios Theodoridis",
"Lars Kai Hansen",
"Zheng-Hua Tan"
] | 2306.00561 | 18,683 | https://openreview.net/forum?id=Q53QLftNkA |
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[] | Poster | [] | Robustness and compactness are two essential attributes of deep learning models that are deployed in the real world. The goals of robustness and compactness may seem to be at odds, since robustness requires generalization across domains, while the process of compression exploits specificity in one domain. We introduce \textit{Adaptive Sharpness-Aware Pruning (AdaSAP)}, which unifies these goals through the lens of network sharpness. The AdaSAP method produces sparse networks that are robust to input variations which are \textit{unseen at training time}. We achieve this by strategically incorporating weight perturbations in order to optimize the loss landscape. This allows the model to be both primed for pruning and regularized for improved robustness. AdaSAP improves the robust accuracy of pruned models on image classification by up to +6\% on ImageNet C and +4\% on ImageNet V2, and on object detection by +4\% on a corrupted Pascal VOC dataset, over a wide range of compression ratios, pruning criteria, and network architectures, outperforming recent pruning art by large margins. | [] | [] | Adaptive Sharpness-Aware Pruning for Robust Sparse Networks | [
"Anna Bair",
"Hongxu Yin",
"Maying Shen",
"Pavlo Molchanov",
"Jose M. Alvarez"
] | 2306.14306 | 18,682 | https://openreview.net/forum?id=QFYVVwiAM8 |
|
[] | Poster | [
"https://github.com/tmlr-group/one-shot-subgraph"
] | To deduce new facts on knowledge graph (KG), a reasoning system learns from the graph structure and collects local evidence to find the answer. However, existing methods suffer from a severe scalability problem due to the utilization of the whole KG for reasoning, which hinders their promise on large-scale KG and cannot be directly addressed by vanilla sampling methods. In this work, we propose the one-shot subgraph reasoning to achieve efficient as well as adaptive KG reasoning. The design principle is that, instead of directly acting on the whole KG, the reasoning procedure is decoupled into two steps, i.e., (i) extracting only one query-dependent subgraph and (ii) reasoning on this single subgraph. We reveal that the non-parametric and computation-efficient heuristics Personalized PageRank (PPR) can effectively identify the potential answers and supports to the reasoning. With the promoted efficiency, we further introduce the subgraph-based searching of optimal configurations in both data and model spaces. Empirically, our method achieves promoted efficiency and also leading performances on five large-scale benchmarks. | [] | [] | Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs | [
"Zhanke Zhou",
"Yongqi Zhang",
"Jiangchao Yao",
"quanming yao",
"Bo Han"
] | 2403.10231 | 18,681 | https://openreview.net/forum?id=QHROe7Mfcb |
|
[] | Spotlight Poster | [] | We present MovingParts, a NeRF-based method for dynamic scene reconstruction and part discovery. We consider motion as an important cue for identifying parts, that all particles on the same part share the common motion pattern. From the perspective of fluid simulation, existing deformation-based methods for dynamic NeRF can be seen as parameterizing the scene motion under the Eulerian view, i.e., focusing on specific locations in space through which the fluid flows as time passes. However, it is intractable to extract the motion of constituting objects or parts using the Eulerian view representation. In this work, we introduce the dual Lagrangian view and enforce representations under the Eulerian/Lagrangian views to be cycle-consistent. Under the Lagrangian view, we parameterize the scene motion by tracking the trajectory of particles on objects. The Lagrangian view makes it convenient to discover parts by factorizing the scene motion as a composition of part-level rigid motions. Experimentally, our method can achieve fast and high-quality dynamic scene reconstruction from even a single moving camera, and the induced part-based representation allows direct applications of part tracking, animation, 3D scene editing, etc. | [] | [] | MovingParts: Motion-based 3D Part Discovery in Dynamic Radiance Field | [
"Kaizhi Yang",
"Xiaoshuai Zhang",
"Zhiao Huang",
"Xuejin Chen",
"Zexiang Xu",
"Hao Su"
] | 2303.05703 | 18,678 | https://openreview.net/forum?id=QQ6RgKYiQq |
|
[] | Poster | [
"https://github.com/apple/ml-mofi"
] | We present MOFI, a new vision foundation model designed to learn image representations from noisy entity annotated images. MOFI differs from previous work in two key aspects: (i) pre-training data, and (ii) training recipe. Regarding data, we introduce a new approach to automatically assign entity labels to images from noisy image-text pairs. Our approach involves employing a named entity recognition model to extract entities from the alt-text, and then using a CLIP model to select the correct entities as labels of the paired image. The approach is simple, does not require costly human annotation, and can be readily scaled up to billions of image-text pairs mined from the web. Through this method, we have created Image-to-Entities (I2E), a new large-scale dataset with 1 billion images and 2 million distinct entities, covering rich visual concepts in the wild. Building upon the I2E dataset, we study different training recipes, including supervised pre-training, contrastive pre-training, and multi-task learning. For constrastive pre-training, we treat entity names as free-form text, and further enrich them with entity descriptions. Experiments show that supervised pre-training with large-scale fine-grained entity labels is highly effective for image retrieval tasks, and multi-task training further improves the performance. The final MOFI model achieves 86.66% mAP on the challenging GPR1200 dataset, surpassing the previous state-of-the-art performance of 72.19% from OpenAI's CLIP model. Further experiments on zero-shot and linear probe image classification also show that MOFI outperforms a CLIP model trained on the original image-text data, demonstrating the effectiveness of the I2E dataset in learning strong image representations. | [] | [] | MOFI: Learning Image Representations from Noisy Entity Annotated Images | [
"Wentao Wu",
"Aleksei Timofeev",
"Chen Chen",
"Bowen Zhang",
"Kun Duan",
"Shuangning Liu",
"Yantao Zheng",
"Jonathon Shlens",
"Xianzhi Du",
"Yinfei Yang"
] | 2306.07952 | 18,677 | https://openreview.net/forum?id=QQYpgReSRk |
|
[] | Poster | [] | Large Language Models (LLMs) trained on large volumes of data excel at various natural language tasks, but they cannot handle tasks requiring knowledge that has not been trained on previously. One solution is to use a retriever that fetches relevant information to expand LLM's knowledge scope. However, existing textual-oriented retrieval-based LLMs are not ideal on structured table data due to diversified data modalities and large table sizes. In this work, we propose OpenTab, an open-domain table reasoning framework powered by LLMs. Overall, OpenTab leverages table retriever to fetch relevant tables and then generates SQL programs to parse the retrieved tables efficiently. Utilizing the intermediate data derived from the SQL executions, it conducts grounded inference to produce accurate response. Extensive experimental evaluation shows that OpenTab significantly outperforms baselines in both open- and closed-domain settings, achieving up to 21.5% higher accuracy. We further run ablation studies to validate the efficacy of our proposed designs of the system. | [] | [] | OpenTab: Advancing Large Language Models as Open-domain Table Reasoners | [
"Kezhi Kong",
"Jiani Zhang",
"Zhengyuan Shen",
"Balasubramaniam Srinivasan",
"Chuan Lei",
"Christos Faloutsos",
"Huzefa Rangwala",
"George Karypis"
] | 2402.14361 | 18,676 | https://openreview.net/forum?id=Qa0ULgosc9 |
|
[] | Poster | [
"https://github.com/ChenxiangMA/AugLocal"
] | Deep neural networks are typically trained using global error signals that backpropagate (BP) end-to-end, which is not only biologically implausible but also suffers from the update locking problem and requires huge memory consumption. Local learning, which updates each layer independently with a gradient-isolated auxiliary network, offers a promising alternative to address the above problems. However, existing local learning methods are confronted with a large accuracy gap with the BP counterpart, particularly for large-scale networks. This is due to the weak coupling between local layers and their subsequent network layers, as there is no gradient communication across layers. To tackle this issue, we put forward an augmented local learning method, dubbed AugLocal. AugLocal constructs each hidden layer’s auxiliary network by uniformly selecting a small subset of layers from its subsequent network layers to enhance their synergy. We also propose to linearly reduce the depth of auxiliary networks as the hidden layer goes deeper, ensuring sufficient network capacity while reducing the computational cost of auxiliary networks. Our extensive experiments on four image classification datasets (i.e., CIFAR-10, SVHN, STL-10, and ImageNet) demonstrate that AugLocal can effectively scale up to tens of local layers with a comparable accuracy to BP-trained networks while reducing GPU memory usage by around 40%. The proposed AugLocal method, therefore, opens up a myriad of opportunities for training high-performance deep neural networks on resource-constrained platforms. Our code will be publicly available. | [] | [] | Scaling Supervised Local Learning with Augmented Auxiliary Networks | [
"Chenxiang Ma",
"Jibin Wu",
"Chenyang Si",
"KC Tan"
] | 2402.17318 | 18,675 | https://openreview.net/forum?id=Qbf1hy8b7m |
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[] | Poster | [] | Graph Neural Networks (GNNs) have become the de facto standard for modeling graph-structured data in various applications. Among them, implicit GNNs have shown a superior ability to effectively capture long-range dependencies in underlying graphs. However, implicit GNNs tend to be computationally expensive and have high memory usage, due to 1) their use of full-batch training; and 2) they require a large number of iterations to solve a fixed-point equation. These compromise the scalability and efficiency of implicit GNNs especially on large graphs. In this paper, we aim to answer the question: how can we efficiently train implicit GNNs to provide effective predictions on large graphs? We propose a new scalable and effective implicit GNN (SEIGNN) with a mini-batch training method and a stochastic solver, which can be trained efficiently on large graphs. Specifically, SEIGNN can more effectively incorporate global and long-range information by introducing coarse-level nodes in the mini-batch training method. It also achieves reduced training time by obtaining unbiased approximate solutions with fewer iterations in the proposed solver. Comprehensive experiments on various large graphs demonstrate that SEIGNN outperforms baselines and achieves higher accuracy with less training time compared with existing implicit GNNs. | [] | [] | Scalable and Effective Implicit Graph Neural Networks on Large Graphs | [
"Juncheng Liu",
"Bryan Hooi",
"Kenji Kawaguchi",
"Yiwei Wang",
"Chaosheng Dong",
"Xiaokui Xiao"
] | 18,674 | https://openreview.net/forum?id=QcMdPYBwTu |
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[] | Poster | [] | We study the problem of learning hierarchical polynomials over the standard Gaussian distribution with three-layer neural networks. We specifically consider target functions of the form $h = g \circ p$ where $p : \mathbb{R}^d \rightarrow \mathbb{R}$ is a degree $k$ polynomial and $g: \mathbb{R} \rightarrow \mathbb{R}$ is a degree $q$ polynomial. This function class generalizes the single-index model, which corresponds to $k=1$, and is a natural class of functions possessing an underlying hierarchical structure. Our main result shows that for a large subclass of degree $k$ polynomials $p$, a three-layer neural network trained via layerwise gradient descent on the square loss learns the target $h$ up to vanishing test error in $\widetilde O(d^k)$ samples and polynomial time. This is a strict improvement over kernel methods, which require $\widetilde \Theta(d^{kq})$ samples, as well as existing guarantees for two-layer networks, which require the target function to be low-rank. Our result also generalizes prior works on three-layer neural networks, which were restricted to the case of $p$ being a quadratic. When $p$ is indeed a quadratic, we achieve the information-theoretically optimal sample complexity $\widetilde O(d^2)$, which is an improvement over prior work (Nichani et al., 2023) requiring a sample size of $\widetilde\Theta(d^4)$. Our proof proceeds by showing that during the first stage of training the network performs feature learning to recover the feature $p$ with $\widetilde O(d^k)$ samples. This work demonstrates the ability of three-layer neural networks to learn complex features and as a result learn a broad class of hierarchical functions. | [] | [] | Learning Hierarchical Polynomials with Three-Layer Neural Networks | [
"Zihao Wang",
"Eshaan Nichani",
"Jason D. Lee"
] | 2311.13774 | 18,673 | https://openreview.net/forum?id=QgwAYFrh9t |
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[] | Poster | [] | We tackle the problem of meta-learning across heterogenous tasks. This problem seeks to extract and generalize transferable meta-knowledge through streaming task sets from a multi-modal task distribution. The extracted meta-knowledge can be used to create predictors for new tasks using a small number of labeled samples. Most meta-learning methods assume a homogeneous task distribution, thus limiting their generalization capacity when handling multi-modal task distributions. Recent work has shown that the generalization of meta-learning depends on the similarity of tasks in the training distribution, and this has led to many clustering approaches that aim to detect homogeneous clusters of tasks. However, these methods suffer from a significant increase in parameter complexity. To overcome this weakness, we propose a new heterogeneous meta-learning strategy that efficiently captures the multi-modality of the task distribution via modulating the routing between convolution channels in the network, instead of directly modulating the network weights. This new mechanism can be cast as a permutation learning problem. We further introduce a novel neural permutation layer based on the classical Benes routing network, which has sub-quadratic parameter complexity in the total number of channels, as compared to the quadratic complexity of the state-of-the-art Gumbel-Sinkhorn layer. We demonstrate our approach on various multi-modal meta-learning benchmarks, showing that our framework outperforms previous methods in both generalization accuracy and convergence speed. | [] | [] | Efficient Heterogeneous Meta-Learning via Channel Shuffling Modulation | [
"Minh Hoang",
"Carl Kingsford"
] | 18,672 | https://openreview.net/forum?id=QiJuMJl0QS |
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[] | Poster | [] | Category-specific models are provenly valuable methods in 3D single object tracking (SOT) regardless of Siamese or motion-centric paradigms. However, such over-specialize model designs incur redundant parameters, thus limiting the broader applicability of 3D SOT task. This paper first introduces unified models that can simultaneously track objects across all categories using a single network with shared model parameters. Specifically, we propose to explicitly encode distinct attributes associated to different object categories, enabling the model to adapt to cross-category data. We discover that the attribute variances of point cloud objects primarily occur from the size and shape (e.g., large and square vehicles vs. small and slender humans). Based on this observation, we design a novel point set representation learning network inheriting transformer architecture, termed AdaFormer, which adaptively encodes the dynamically varying shape and size information from cross-category data in a unified manner. We further incorporate the size and shape prior derived from the known template targets into the model’s inputs and learning objective, facilitating the learning of unified representation. Equipped with such designs, we construct two unified models SiamCUT and MoCUT, following the Siamese and motion-centric paradigms, respectively. Extensive experiments demonstrate that the proposed unified models exhibit strong generalization and stability. Furthermore, our unified models outperform the category-specific counterparts by a significant margin (e.g., on KITTI dataset, 12% and 3% performance gains on the Siamese and motion paradigms, respectively). Code and models will be released. | [] | [] | Towards Category Unification of 3D Single Object Tracking on Point Clouds | [
"Jiahao Nie",
"Zhiwei He",
"Xudong Lv",
"Xueyi Zhou",
"Dong-Kyu Chae",
"Fei Xie"
] | 2401.11204 | 18,670 | https://openreview.net/forum?id=QlqdXrzzD1 |
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[] | Poster | [] | This work focuses on leveraging and selecting from vast, unlabeled, open data to \emph{pre-fine-tune} a pre-trained language model. The goal is to minimize the need for costly domain-specific data for subsequent fine-tuning while achieving desired performance levels. While many data selection algorithms have been designed for small-scale applications, rendering them unsuitable for our context, some emerging methods do cater to language data scales. However, they often prioritize data that aligns with the target distribution. While this strategy may be effective when training a model from scratch, it can yield limited results when the model has already been pre-trained on a different distribution. Differing from prior work, our key idea is to select data that nudges the pre-training distribution closer to the target distribution. We show the optimality of this approach for fine-tuning tasks under certain conditions. We demonstrate the efficacy of our methodology across a diverse array of tasks, showing that it consistently surpasses other selection methods. Moreover, our proposed method is significantly faster than existing techniques, scaling to millions of samples within a single GPU hour. Our code is open-sourced \footnote{Code repository: \url{https://anonymous.4open.science/r/DV4LLM-D761/}}. While fine-tuning offers significant potential for enhancing performance across diverse tasks, its associated costs often limit its widespread adoption; with this work, we hope to lay the groundwork for cost-effective fine-tuning, making its benefits more accessible. | [] | [] | Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs | [
"Feiyang Kang",
"Hoang Anh Just",
"Yifan Sun",
"Himanshu Jahagirdar",
"Yuanzhi Zhang",
"Rongxing Du",
"Anit Kumar Sahu",
"Ruoxi Jia"
] | 18,669 | https://openreview.net/forum?id=QmYNBVukex |
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[] | Poster | [] | The video-language (VL) pretraining has achieved remarkable improvement in multiple downstream tasks. However, the current VL pretraining framework is hard to extend to multiple modalities (N modalities, $N\geq3$) beyond vision and language. We thus propose LanguageBind, taking the language as the bind across different modalities because the language modality is well-explored and contains rich semantics. Specifically, we freeze the language encoder acquired by VL pretraining, then train encoders for other modalities with contrastive learning. As a result, all modalities are mapped to a shared feature space, implementing multi-modal semantic alignment. While LanguageBind ensures that we can extend VL modalities to N modalities, we also need a high-quality dataset with alignment data pairs centered on language. We thus propose VIDAL-10M with Video, Infrared, Depth, Audio and their corresponding Language, naming as VIDAL-10M. In our VIDAL-10M, all videos are from short video platforms with complete semantics rather than truncated segments from long videos, and all the video, depth, infrared, and audio modalities are aligned to their textual descriptions. After pretraining on VIDAL-10M, we outperform ImageBind by 1.2% R@1 on the MSR-VTT dataset with only 15% of the parameters in the zero-shot video-text retrieval, validating the high quality of our dataset. Beyond this, our LanguageBind has achieved great improvement in the zero-shot video, audio, depth, and infrared understanding tasks. For instance, on the LLVIP and NYU-D datasets, LanguageBind outperforms ImageBind-huge with 23.8% and 11.1% top-1 accuracy. | [] | [] | LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment | [
"Bin Zhu",
"Bin Lin",
"Munan Ning",
"Yang Yan",
"Jiaxi Cui",
"WANG HongFa",
"Yatian Pang",
"Wenhao Jiang",
"Junwu Zhang",
"Zongwei Li",
"Cai Wan Zhang",
"Zhifeng Li",
"Wei Liu",
"Li Yuan"
] | 18,668 | https://openreview.net/forum?id=QmZKc7UZCy |
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[] | Poster | [] | Text-to-Image Diffusion Models such as Stable-Diffusion and Imagen have achieved unprecedented quality of photorealism with state-of-the-art FID scores on MS-COCO and other generation benchmarks. Given a caption, image generation requires fine-grained knowledge about attributes such as object structure, style, and viewpoint amongst others. Where does this information reside in text-to-image generative models? In our paper, we tackle this question and understand how knowledge corresponding to distinct visual attributes is stored in large-scale text-to-image diffusion models. We adapt Causal Mediation Analysis for text-to-image models and trace knowledge about distinct visual attributes to various (causal) components in the (i) UNet and (ii) text-encoder of the diffusion model. In particular, we show that unlike large-language models, knowledge about different attributes is not localized in isolated components, but is instead distributed amongst a set of components in the conditional UNet. These sets of components are often distinct for different visual attributes (e.g., style} / objects). Remarkably, we find that the text-encoder in public text-to-image models such as Stable-Diffusion contains {\it only} one causal state across different visual attributes, and this is the first self-attention layer corresponding to the last subject token of the attribute in the caption. This is in stark contrast to the causal states in other language models which are often the mid-MLP layers. Based on this observation of only one causal state in the text-encoder, we introduce a fast, data-free model editing method DiffQuickFix which can effectively edit concepts (remove or update knowledge) in text-to-image models. DiffQuickFix can edit (ablate) concepts in under a second with a closed-form update, providing a significant 1000x speedup and comparable editing performance to existing fine-tuning based editing methods. | [] | [] | Localizing and Editing Knowledge In Text-to-Image Generative Models | [
"Samyadeep Basu",
"Nanxuan Zhao",
"Vlad I Morariu",
"Soheil Feizi",
"Varun Manjunatha"
] | 2310.13730 | 18,667 | https://openreview.net/forum?id=Qmw9ne6SOQ |
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[] | Poster | [] | In numerous artificial intelligence applications, the collaborative efforts of multiple intelligent agents are imperative for the successful attainment of target objectives. To enhance coordination among these agents, a distributed communication framework is often employed. However, information sharing among all agents proves to be resource-intensive, while the adoption of a manually pre-defined communication architecture imposes limitations on inter-agent communication, thereby constraining the potential for collaborative efforts. In this study, we introduce a novel approach wherein we conceptualize the communication architecture among agents as a learnable graph. We formulate this problem as the task of determining the communication graph while enabling the architecture parameters to update normally, thus necessitating a bi-level optimization process. Utilizing continuous relaxation of the graph representation and incorporating attention units, our proposed approach, CommFormer, efficiently optimizes the communication graph and concurrently refines architectural parameters through gradient descent in an end-to-end manner. Extensive experiments on StarCraftII combat games substantiate the robustness of our model across diverse cooperative scenarios, where agents are able to develop more coordinated and sophisticated strategies regardless of changes in the number of agents. | [] | [] | Learning Multi-Agent Communication from Graph Modeling Perspective | [
"Shengchao Hu",
"Li Shen",
"Ya Zhang",
"Dacheng Tao"
] | 18,666 | https://openreview.net/forum?id=Qox9rO0kN0 |
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[] | Poster | [] | Recent studies have successfully shown that large language models (LLMs) can be successfully used for generative error correction (GER) on top of the automatic speech recognition (ASR) output. Specifically, an LLM is utilized to carry out a direct mapping from the N-best hypotheses list generated by an ASR system to the predicted output transcription. However, despite its effectiveness, GER introduces extra data uncertainty since the LLM is trained without taking into account acoustic information available in the speech signal. In this work, we aim to overcome such a limitation by infusing acoustic information before generating the predicted transcription through a novel late fusion solution termed Uncertainty-Aware Dynamic Fusion (UADF). UADF is a multimodal fusion approach implemented into an auto-regressive decoding process and works in two stages: (i) It first analyzes and calibrates the token-level LLM decision, and (ii) it then dynamically assimilates the information from the acoustic modality. Experimental evidence collected from various ASR tasks shows that UADF surpasses existing fusion mechanisms in several ways. It yields significant improvements in word error rate (WER) while mitigating data uncertainty issues in LLM and addressing the poor generalization relied with sole modality during fusion. We also demonstrate that UADF seamlessly adapts to audio-visual speech recognition. | [] | [] | It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition | [
"CHEN CHEN",
"Ruizhe Li",
"Yuchen Hu",
"Sabato Marco Siniscalchi",
"Pin-Yu Chen",
"Ensiong Chng",
"Chao-Han Huck Yang"
] | 2402.05457 | 18,665 | https://openreview.net/forum?id=QqjFHyQwtF |
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[] | Poster | [] | In this work, we study the challenge of providing human-understandable descriptions for failure modes in trained image classification models.Existing works address this problem by first identifying clusters (or directions) of incorrectly classified samples in a latent space and then aiming to provide human-understandable text descriptions for them.We observe that in some cases, describing text does not match wellwith identified failure modes, partially owing to the fact that shared interpretable attributes of failure modes may not be captured using clustering in the feature space.To improve on these shortcomings, we propose a novel approach that prioritizes interpretability in this problem: we start by obtaining human-understandable concepts (tags) of images in the dataset andthen analyze the model's behavior based on the presence or absence of combinations of these tags.Our method also ensures that the tags describing a failure mode form a minimal set,avoiding redundant and noisy descriptions.Through several experiments on different datasets, we show that our method successfully identifies failure modes and generates high-quality text descriptions associated with them.These results highlight the importance of prioritizing interpretability in understanding model failures. | [] | [] | PRIME: Prioritizing Interpretability in Failure Mode Extraction | [
"Keivan Rezaei",
"Mehrdad Saberi",
"Mazda Moayeri",
"Soheil Feizi"
] | 2310.00164 | 18,664 | https://openreview.net/forum?id=QrEHs9w5UF |
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[] | Poster | [] | ML-augmented algorithms utilize predictions to achieve performance beyond their worst-case bounds. Producing these predictions might be a costly operation – this motivated Im et al. [2022] to introduce the study of algorithms which use predictions parsimoniously. We design parsimonious algorithms for caching and MTS with action predictions, proposed by Antoniadis et al. [2023], focusing on the parameters of consistency (performance with perfect predictions) and smoothness (dependence of their performance on prediction error). Our algorithm for caching is 1-consistent, robust, and its smoothness deteriorates with decreasing number of available predictions. We propose an algorithm for general MTS whose consistency and smoothness both scale linearly with the decreasing number of predictions. Without restriction on the number of available predictions, both algorithms match the earlier guarantees achieved by Antoniadis et al. [2023]. | [] | [] | Algorithms for Caching and MTS with reduced number of predictions | [
"Karim Ahmed Abdel Sadek",
"Marek Elias"
] | 2404.06280 | 18,663 | https://openreview.net/forum?id=QuIiLSktO4 |
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[] | Poster | [] | Rendering scenes observed in a monocular video from novel viewpoints is a challenging problem. For static scenes the community has studied both scene-specific optimization techniques, which optimize on every test scene, and generalized techniques, which only run a deep net forward pass on a test scene. In contrast, for dynamic scenes, scene-specific optimization techniques exist, but, to our best knowledge, there is currently no generalized method for dynamic novel view synthesis from a given monocular video. To explore whether generalized dynamic novel view synthesis from monocular videos is possible today, we establish an analysis framework based on existing techniques and work toward the generalized approach. We find a pseudo-generalized process without scene-specific \emph{appearance} optimization is possible, but geometrically and temporally consistent depth estimates are needed. Despite no scene-specific appearance optimization, the pseudo-generalized approach improves upon some scene-specific methods.For more information see project page at https://xiaoming-zhao.github.io/projects/pgdvs. | [] | [] | Pseudo-Generalized Dynamic View Synthesis from a Video | [
"Xiaoming Zhao",
"R Alex Colburn",
"Fangchang Ma",
"Miguel Ángel Bautista",
"Joshua M. Susskind",
"Alex Schwing"
] | 2310.08587 | 18,662 | https://openreview.net/forum?id=QuVlUn4T2G |
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[] | Spotlight Poster | [] | PyTorch has ascended as a premier machine learning framework, yet it lacks a native and comprehensive library for decision and control tasks suitable for large development teams dealing with complex real-world data and environments. To address this issue, we propose TorchRL, a generalistic control library for PyTorch that provides well-integrated, yet standalone components. We introduce a new and flexible PyTorch primitive, the TensorDict, which facilitates streamlined algorithm development across the many branches of Reinforcement Learning (RL) and control. We provide a detailed description of the building blocks and an extensive overview of the library across domains and tasks. Finally, we experimentally demonstrate its reliability and flexibility, and show comparative benchmarks to demonstrate its computational efficiency. TorchRL fosters long-term support and is publicly available on GitHub for greater reproducibility and collaboration within the research community. The code is open-sourced on GitHub. | [] | [] | TorchRL: A data-driven decision-making library for PyTorch | [
"Albert Bou",
"Matteo Bettini",
"Sebastian Dittert",
"Vikash Kumar",
"Shagun Sodhani",
"Xiaomeng Yang",
"Gianni De Fabritiis",
"Vincent Moens"
] | 2306.00577 | 18,660 | https://openreview.net/forum?id=QxItoEAVMb |
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[] | Poster | [] | Spatio-temporal graph (STG) learning is foundational for smart city applications, yet it is often hindered by data scarcity in many cities and regions. To bridge this gap, we propose a novel generative pre-training framework, GPDiff, for STG transfer learning. Unlike conventional approaches that heavily rely on common feature extraction or intricate transfer learning designs, our solution takes a novel approach by performing generative pre-training on a collection of model parameters optimized with data from source cities. We recast STG transfer learning as pre-training a generative hypernetwork, which generates tailored model parameters guided by prompts, allowing for adaptability to diverse data distributions and city-specific characteristics. GPDiff employs a diffusion model with a transformer-based denoising network, which is model-agnostic to integrate with powerful STG models. By addressing challenges arising from data gaps and the complexity of generalizing knowledge across cities, our framework consistently outperforms state-of-the-art baselines on multiple real-world datasets for tasks such as traffic speed prediction and crowd flow prediction. The implementation of our approach is available: https://anonymous.4open.science/r/GPDiff. | [] | [] | Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation | [
"Yuan Yuan",
"Chenyang Shao",
"Jingtao Ding",
"Depeng Jin",
"Yong Li"
] | 2402.11922 | 18,659 | https://openreview.net/forum?id=QyFm3D3Tzi |
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[] | Spotlight Poster | [] | The current electroencephalogram (EEG) based deep learning models are typically designed for specific datasets and applications in brain-computer interaction (BCI), limiting the scale of the models and thus diminishing their perceptual capabilities and generalizability. Recently, Large Language Models (LLMs) have achieved unprecedented success in text processing, prompting us to explore the capabilities of Large EEG Models (LEMs). We hope that LEMs can break through the limitations of different task types of EEG datasets, and obtain universal perceptual capabilities of EEG signals through unsupervised pre-training. Then the models can be fine-tuned for different downstream tasks. However, compared to text data, the volume of EEG datasets is generally small and the format varies widely. For example, there can be mismatched numbers of electrodes, unequal length data samples, varied task designs, and low signal-to-noise ratio. To overcome these challenges, we propose a unified foundation model for EEG called Large Brain Model (LaBraM). LaBraM enables cross-dataset learning by segmenting the EEG signals into EEG channel patches. Vector-quantized neural spectrum prediction is used to train a semantically rich neural tokenizer that encodes continuous raw EEG channel patches into compact neural codes. We then pre-train neural Transformers by predicting the original neural codes for the masked EEG channel patches. The LaBraMs were pre-trained on about 2,500 hours of various types of EEG signals from around 20 datasets and validated on multiple different types of downstream tasks. Experiments on abnormal detection, event type classification, emotion recognition, and gait prediction show that our LaBraM outperforms all compared SOTA methods in their respective fields. Our code is available at https://github.com/935963004/LaBraM. | [] | [] | Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI | [
"Weibang Jiang",
"Liming Zhao",
"Bao-liang Lu"
] | 18,658 | https://openreview.net/forum?id=QzTpTRVtrP |
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[] | Poster | [] | Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities. Recently, many studies have focused on the tool utilization ability of LLMs. They primarily investigated how LLMs effectively collaborate with given specific tools. However, in scenarios where LLMs serve as intelligent agents, as seen in applications like AutoGPT and MetaGPT, LLMs are expected to engage in intricate decision-making processes that involve deciding whether to employ a tool and selecting the most suitable tool(s) from a collection of available tools to fulfill user requests. Therefore, in this paper, we introduce MetaTool, a benchmark designed to evaluate whether LLMs have tool usage awareness and can correctly choose tools. Specifically, we create a dataset called ToolE within the benchmark. This dataset contains various types of user queries in the form of prompts that trigger LLMs to use tools, including both single-tool and multi-tool scenarios. Subsequently, we set the tasks for both tool usage awareness and tool selection. We define four subtasks from different perspectives in tool selection, including tool selection with similar choices, tool selection in specific scenarios, tool selection with possible reliability issues, and multi-tool selection. We conduct experiments involving nine popular LLMs and find that the majority of them still struggle to effectively select tools, highlighting the existing gaps between LLMs and genuine intelligent agents. However, through the error analysis, we found there is still significant room for improvement. Finally, we conclude with insights for tool developers that follow ChatGPT to provide detailed descriptions that can enhance the tool selection performance of LLMs. | [] | [] | MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use | [
"Yue Huang",
"Jiawen Shi",
"Yuan Li",
"Chenrui Fan",
"Siyuan Wu",
"Qihui Zhang",
"Yixin Liu",
"Pan Zhou",
"Yao Wan",
"Neil Zhenqiang Gong",
"Lichao Sun"
] | 2310.03128 | 18,657 | https://openreview.net/forum?id=R0c2qtalgG |
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[] | Poster | [] | Imitation learning considerably simplifies policy synthesis compared to alternative approaches by exploiting access to expert demonstrations. For such imitation policies, errors away from the training samples are particularly critical. Even rare slip-ups in the policy action outputs can compound quickly over time, since they lead to unfamiliar future states where the policy is still more likely to err, eventually causing task failures. We revisit simple supervised "behavior cloning" for conveniently training the policy from nothing more than pre-recorded demonstrations, but carefully design the model class to counter the compounding error phenomenon. Our "memory-consistent neural network" (MCNN) outputs are hard-constrained to stay within clearly specified permissible regions anchored to prototypical "memory" training samples. We provide a guaranteed upper bound for the sub-optimality gap induced by MCNN policies. Using MCNNs on 9 imitation learning tasks, with MLP, Transformer, and Diffusion backbones, spanning dexterous robotic manipulation and driving, proprioceptive inputs and visual inputs, and varying sizes and types of demonstration data, we find large and consistent gains in performance, validating that MCNNs are better-suited than vanilla deep neural networks for imitation learning applications | [] | [] | Memory-Consistent Neural Networks for Imitation Learning | [
"Kaustubh Sridhar",
"Souradeep Dutta",
"Dinesh Jayaraman",
"James Weimer",
"Insup Lee"
] | 2310.06171 | 18,656 | https://openreview.net/forum?id=R3Tf7LDdX4 |
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[] | Poster | [] | Human intelligence is adept at absorbing valuable insights from external knowledge.This capability is equally crucial for artificial intelligence. In contrast, classical reinforcement learning agents lack such capabilities and often resort to extensive trial and error to explore the environment. This paper introduces $\textbf{PAE}$: $\textbf{P}$lanner-$\textbf{A}$ctor-$\textbf{E}$valuator, a novel framework for teaching agents to $\textit{learn to absorb external knowledge}$. PAE integrates the Planner's knowledge-state alignment mechanism, the Actor's mutual information skill control, and the Evaluator's adaptive intrinsic exploration reward to achieve 1) effective cross-modal information fusion, 2) enhanced linkage between knowledge and state, and 3) hierarchical mastery of complex tasks.Comprehensive experiments in six challenging sparse reward environments demonstrate PAE's superior exploration efficiency with good interpretability compared to existing methods. We provide the source code in the supplementary for further study and application. | [] | [] | PAE: Reinforcement Learning from External Knowledge for Efficient Exploration | [
"Zhe Wu",
"Haofei Lu",
"Junliang Xing",
"You Wu",
"Renye Yan",
"Yaozhong Gan",
"Yuanchun Shi"
] | 18,655 | https://openreview.net/forum?id=R7rZUSGOPD |
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[] | Poster | [
"https://github.com/kai422/SCALE"
] | The capacity of a modern deep learning system to determine if a sample falls within its realm of knowledge is fundamental and important.In this paper, we offer insights and analyses of recent state-of-the-art out-of-distribution (OOD) detection methods - extremely simple activation shaping (ASH). We demonstrate that activation pruning has a detrimental effect on OOD detection, while activation scaling enhances it. Moreover, we propose SCALE, a simple yet effective post-hoc network enhancement method for OOD detection, which attains state-of-the-art OOD detection performance without compromising in-distribution (ID) accuracy. By integrating scaling concepts into the training process to capture a sample's ID characteristics, we propose Intermediate Tensor SHaping (ISH), a lightweight method for training time OOD detection enhancement. We achieve AUROC scores of +1.85\% for near-OOD and +0.74\% for far-OOD datasets on the OpenOOD v1.5 ImageNet-1K benchmark. | [] | [] | Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement | [
"Kai Xu",
"Rongyu Chen",
"Gianni Franchi",
"Angela Yao"
] | 2310.00227 | 18,654 | https://openreview.net/forum?id=RDSTjtnqCg |
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[] | Poster | [] | In this paper, we first challenge the common premise that linear MDPs always induce performance guarantees independent of the state space. We prove that, in linear MDPs, the feature dimension $d$ is lower bounded by $S/U$ in order to aptly represent transition probabilities, where $S$ is the size of the state space and $U$ is the maximum size of directly reachable states.Hence, $d$ can still scale with $S$ depending on the direct reachability of the environment. To address this limitation of linear MDPs, we propose a novel structural aggregation framework based on dynamics, named as the *dynamics aggregation*. For this newly proposed framework, we design a provably efficient hierarchical reinforcement learning algorithm in linear function approximation that leverages aggregated sub-structures. Our proposed algorithm exhibits statistical efficiency, achieving a regret of $\tilde{O} \big( d_{\psi}^{3/2} H^{3/2}\sqrt{ NT} \big)$, where $d_{\psi}$ represents the feature dimension of *aggregated subMDPs* and $N$ signifies the number of aggregated subMDPs. We establish that the condition $d_{\psi}^3 N \ll d^{3}$ is readily met in most real-world environments with hierarchical structures, enabling a substantial improvement in the regret bound compared to LSVI-UCB, which enjoys a regret of $\tilde{O}(d^{3/2} H^{3/2} \sqrt{ T})$. To the best of our knowledge, this work presents the first HRL algorithm with linear function approximation that offers provable guarantees. | [] | [] | Demystifying Linear MDPs and Novel Dynamics Aggregation Framework | [
"Joongkyu Lee",
"Min-hwan Oh"
] | 18,653 | https://openreview.net/forum?id=RDSj6S8WJe |
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[] | Poster | [] | Current training pipelines in object recognition neglect Hue Jittering when doing data augmentation as it not only brings appearance changes that are detrimental to classification, but also the implementation is inefficient in practice. In this study, we investigate the effect of hue variance in the context of video recognition and find this variance to be beneficial since static appearances are less important in videos that contain motion information. Based on this observation, we propose a data augmentation method for video recognition, named Motion Coherent Augmentation (MCA), that introduces appearance variation in videos and implicitly encourages the model to prioritize motion patterns, rather than static appearances. Concretely, we propose an operation SwapMix to efficiently modify the appearance of video samples, and introduce Variation Alignment (VA) to resolve the distribution shift caused by SwapMix, enforcing the model to learn appearance invariant representations. Comprehensive empirical validations across various architectures and different datasets solidly demonstrate the effectiveness and generalization ability of MCA (e.g., 1.95% average performance gain at different frames on Something-Something V1 dataset over the competing method Uniformer). | [] | [] | Don't Judge by the Look: Towards Motion Coherent Video Representation | [
"Yitian Zhang",
"Yue Bai",
"Huan Wang",
"Yizhou Wang",
"Yun Fu"
] | 18,651 | https://openreview.net/forum?id=RIcYTbpO38 |
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[] | Poster | [] | Generating graphs from a target distribution is a significant challenge across many domains, including drug discovery and social network analysis. In this work, we introduce a novel graph generation method leveraging $K^2$ representation, originally designed for lossless graph compression. The $K^2$ representation enables compact generation while concurrently capturing an inherent hierarchical structure of a graph. In addition, we make contributions by (1) presenting a sequential $K^2$ representation that incorporates pruning, flattening, and tokenization processes and (2) introducing a Transformer-based architecture designed to generate the sequence by incorporating a specialized tree positional encoding scheme. Finally, we extensively evaluate our algorithm on four general and two molecular graph datasets to confirm its superiority for graph generation. | [] | [] | Graph Generation with $K^2$-trees | [
"Yunhui Jang",
"Dongwoo Kim",
"Sungsoo Ahn"
] | 18,652 | https://openreview.net/forum?id=RIEW6M9YoV |
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[] | Poster | [] | As large language models (LLMs) are adopted as a fundamental component of language technologies, it is crucial to accurately characterize their performance. Because choices in prompt design can strongly influence model behavior, this design process is critical in effectively using any modern pre-trained generative language model. In this work, we focus on LLM sensitivity to a quintessential class of meaning-preserving design choices: prompt formatting. We find that several widely used open-source LLMs are extremely sensitive to subtle changes in prompt formatting in few-shot settings, with performance differences of up to 76 accuracy points when evaluated using LLaMA-2-13B. Sensitivity remains even when increasing model size, the number of few-shot examples, or performing instruction tuning. Our analysis suggests that work evaluating LLMs with prompting-based methods would benefit from reporting a range of performance across plausible prompt formats, instead of the currently-standard practice of reporting performance on a single format. We also show that format performance only weakly correlates between models, which puts into question the methodological validity of comparing models with an arbitrarily chosen, fixed prompt format. To facilitate systematic analysis we propose FormatSpread, an algorithm that rapidly evaluates a sampled set of plausible prompt formats for a given task, and reports the interval of expected performance without accessing model weights. Furthermore, we present a suite of analyses that characterize the nature of this sensitivity, including exploring the influence of particular atomic perturbations and the internal representation of particular formats. | [] | [] | Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting | [
"Melanie Sclar",
"Yejin Choi",
"Yulia Tsvetkov",
"Alane Suhr"
] | 2310.11324 | 18,650 | https://openreview.net/forum?id=RIu5lyNXjT |
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[] | Poster | [
"https://github.com/BorealisAI/ConR"
] | Imbalanced distributions are ubiquitous in real-world data. They create constraints on Deep Neural Networks to represent the minority labels and avoid bias towards majority labels. The extensive body of imbalanced approaches address categorical label spaces but fail to effectively extend to regression problems where the label space is continuous. Local and global correlations among continuous labels provide valuable insights towards effectively modelling relationships in feature space. In this work, we propose ConR, a contrastive regularizer that models global and local label similarities in feature space and prevents the features of minority samples from being collapsed into their majority neighbours. ConR discerns the disagreements between the label space and feature space, and imposesa penalty on these disagreements. ConR minds the continuous nature of label space with two main strategies in a contrastive manner: incorrect proximities are penalized proportionate to the label similarities and the correct ones are encouraged to model local similarities. ConR consolidates essential considerations into a generic, easy-to-integrate, and efficient method that effectively addresses deep imbalanced regression. Moreover, ConR is orthogonal to existing approaches and smoothly extends to uni- and multi-dimensional label spaces. Our comprehensive experiments show that ConR significantly boosts the performance of all the state-of-the-art methods on four large-scale deep imbalanced regression benchmarks. | [] | [] | ConR: Contrastive Regularizer for Deep Imbalanced Regression | [
"Mahsa Keramati",
"Lili Meng",
"R. David Evans"
] | 2309.06651 | 18,649 | https://openreview.net/forum?id=RIuevDSK5V |
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[] | Poster | [] | Current state-of-the-art methods for object detection rely on annotated bounding boxes of large data sets for training. However, obtaining such annotations is expensive and can require up to hundreds of hours of manual labor. This poses a challenge, especially since such annotations can only be provided by experts, as they require knowledge about the scientific domain. To tackle this challenge, we propose a domain-specific weakly supervised object detection algorithm that only relies on image-level annotations, which are significantly easier to acquire. Our method distills the knowledge of a pre-trained model, on the task of predicting the presence or absence of a virus in an image, to obtain a set of pseudo-labels that can be used to later train a state-of-the-art object detection model. To do so, we use an optimization approach with a shrinking receptive field to extract virus particles directly without specific network architectures. Through a set of extensive studies, we show how the proposed pseudo-labels are easier to obtain, and, more importantly, are able to outperform other existing weak labeling methods, and even ground truth labels, in cases where the time to obtain the annotation is limited. | [] | [] | Weakly Supervised Virus Capsid Detection with Image-Level Annotations in Electron Microscopy Images | [
"Hannah Kniesel",
"Leon Sick",
"Tristan Payer",
"Tim Bergner",
"Kavitha Shaga Devan",
"Clarissa Read",
"Paul Walther",
"Timo Ropinski",
"Pedro Hermosilla"
] | 18,648 | https://openreview.net/forum?id=RJDjSXNuAZ |
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[] | Poster | [] | Sorting is a fundamental operation of all computer systems, having been a long-standing significant research topic. Beyond the problem formulation of traditional sorting algorithms, we consider sorting problems for more abstract yet expressive inputs, e.g., multi-digit images and image fragments, through a neural sorting network. To learn a mapping from a high-dimensional input to an ordinal variable, the differentiability of sorting networks needs to be guaranteed. In this paper we define a softening error by a differentiable swap function, and develop an error-free swap function that holds non-decreasing and differentiability conditions. Furthermore, a permutation-equivariant Transformer network with multi-head attention is adopted to capture dependency between given inputs and also leverage its model capacity with self-attention. Experiments on diverse sorting benchmarks show that our methods perform better than or comparable to baseline methods. | [] | [] | Generalized Neural Sorting Networks with Error-Free Differentiable Swap Functions | [
"Jungtaek Kim",
"Jeongbeen Yoon",
"Minsu Cho"
] | 2310.07174 | 18,647 | https://openreview.net/forum?id=RLSWbk9kPw |
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[] | Poster | [
"https://github.com/YifeiZhou02/HNPG"
] | Hybrid RL is the setting where an RL agent has access to both offline data and online data by interacting with the real-world environment. In this work, we propose a new hybrid RL algorithm that combines an on-policy actor-critic method with offline data. On-policy methods such as policy gradient and natural policy gradient (NPG) have shown to be more robust to model misspecification, though sometimes it may not be as sample efficient as methods that rely on off-policy learning. On the other hand, offline methods that depend on off-policy training often require strong assumptions in theory and are less stable to train in practice. Our new approach integrates a procedure of off-policy training on the offline data into an on-policy NPG framework. We show that our approach, in theory, can obtain a *best-of-both-worlds* type of result --- it achieves the state-of-art theoretical guarantees of offline RL when offline RL-specific assumptions hold, while at the same time maintaining the theoretical guarantees of on-policy NPG regardless of the offline RL assumptions' validity. Experimentally, in challenging rich-observation environments, we show that our approach outperforms a state-of-the-art hybrid RL baseline which only relies on off-policy policy optimization, demonstrating the empirical benefit of combining on-policy and off-policy learning. | [] | [] | Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees | [
"Yifei Zhou",
"Ayush Sekhari",
"Yuda Song",
"Wen Sun"
] | 2311.08384 | 18,646 | https://openreview.net/forum?id=RMgqvQGTwH |
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[] | Poster | [] | This paper proposes a new framework of algorithms that is extended from the celebrated extragradient algorithm. Min-max problem has attracted increasing attention because of application in machine learning missions such as generative adversarial network (GAN) training. While there has been exhaustive researches on convex-concave setting, problem on nonconvex-nonconcave setting faces many challenges, such as convergence to limit cycles. Since general min-max optimization is proved intractable, recent research focus has been put on structured problems. One of these follows the weak Minty variational inequality (weak MVI), which is motivated by relaxing Minty variational inequality without compromising convergence guarantee of extragradient algorithm. Existing extragradient-type algorithms involve one exploration step and one update step per iteration. We analyze the algorithms with multiple exploration steps and show that current assumption can be further relaxed when more exploration is introduced. Furthermore, we design an adaptive algorithm that explores until the optimal improvement is achieved. This process exploit information from the whole trajectory and effectively tackle cyclic behaviors. | [] | [] | Weaker MVI Condition: Extragradient Methods with Multi-Step Exploration | [
"Yifeng Fan",
"Yongqiang Li",
"Bo Chen"
] | 18,645 | https://openreview.net/forum?id=RNGUbTYSjk |
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[] | Poster | [] | Given a sequence of functions $f_1,\ldots,f_n$ with $f_i:\mathcal{D}\mapsto \mathbb{R}$, finite-sum minimization seeks a point ${x}^\star \in \mathcal{D}$ minimizing $\sum_{j=1}^nf_j(x)/n$. In this work, we propose a key twist into the finite-sum minimization, dubbed as *instance-optimal finite-sum minimization*, that asks for a sequence of points $x_1^\star, \ldots, x_n^\star \in D$ such that each ${x}^\star_i \in D$ minimizes the prefix-sum $\sum_{j=1}^if_j(x)/i$. Assuming that each prefix-sum is strongly convex, we develop a first-order stochastic instance optimal gradient method $\mathrm{SIOPT}-\mathrm{Grad}$ producing an $\epsilon$-optimal sequence with $\tilde{\mathcal{O}}(n/\epsilon^{1/3} + 1/\sqrt{\epsilon})$ overall *first-order oracles* (FO). An FO corresponds to the computation of a single gradient $\nabla f_j(x)$ at a given $x \in \mathcal{D}$ for some $j \in [n]$. Our approach significantly improves upon the $\mathcal{O}(n/\epsilon)$ FOs that $\mathrm{StochasticGradientDescent}$ requires and the $\mathcal{O}(n^2 \log (1/\epsilon))$ FOs that state-of-the-art variance reduction methods such as $\mathrm{Katyusha}$ require. We also prove that there is no natural first-order method with $\mathcal{O}\left(n/\epsilon^\alpha\right)$ gradient complexity for $\alpha < 1/4$, establishing that the first-order complexity of our method is nearly tight. | [] | [] | Efficient Continual Finite-Sum Minimization | [
"Ioannis Mavrothalassitis",
"Stratis Skoulakis",
"Leello Tadesse Dadi",
"Volkan Cevher"
] | 18,644 | https://openreview.net/forum?id=RR70yWYenC |
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[] | Poster | [] | The full potential of large pretrained models remains largely untapped in control domains like robotics. This is mainly because of the scarcity of data and the computational challenges associated with training or fine-tuning these large models for such applications. Prior work mainly emphasizes effective \emph{pretraining} of large models for decision-making, with little exploration into how to perform data-efficient continual \emph{adaptation} of these models for new tasks. Recognizing these constraints, we introduce TAIL (Task-specific Adapters for Imitation Learning), a framework for efficient adaptation to new control tasks. Inspired by recent advancements in parameter-efficient fine-tuning in language domains, we explore efficient fine-tuning techniques---e.g., Bottleneck Adapters, P-Tuning, and Low-Rank Adaptation (LoRA)---in TAIL to adapt large pretrained models for new tasks with limited demonstration data. Our extensive experiments comparing prevalent parameter-efficient fine-tuning techniques and adaptation baselines suggest that TAIL with LoRA can achieve the best post-adaptation performance with only 1\% of the trainable parameters of full fine-tuning, while avoiding catastrophic forgetting and preserving adaptation plasticity in continual learning settings. | [] | [] | TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models | [
"Zuxin Liu",
"Jesse Zhang",
"Kavosh Asadi",
"Yao Liu",
"Ding Zhao",
"Shoham Sabach",
"Rasool Fakoor"
] | 2310.05905 | 18,642 | https://openreview.net/forum?id=RRayv1ZPN3 |
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[] | Poster | [] | During periods of quiescence, such as sleep, neural activity in many brain circuits resembles that observed during periods of task engagement. However, the precise conditions under which task-optimized networks can autonomously reactivate the same network states responsible for online behavior is poorly understood. In this study, we develop a mathematical framework that outlines sufficient conditions for the emergence of neural reactivation in circuits that encode features of smoothly varying stimuli. We demonstrate mathematically that noisy recurrent networks optimized to track environmental state variables using change-based sensory information naturally develop denoising dynamics, which, in the absence of input, cause the network to revisit state configurations observed during periods of online activity. We validate our findings using numerical experiments on two canonical neuroscience tasks: spatial position estimation based on self-motion cues, and head direction estimation based on angular velocity cues. Overall, our work provides theoretical support for modeling offline reactivation as an emergent consequence of task optimization in noisy neural circuits. | [] | [] | Sufficient conditions for offline reactivation in recurrent neural networks | [
"Nanda H Krishna",
"Colin Bredenberg",
"Daniel Levenstein",
"Blake Aaron Richards",
"Guillaume Lajoie"
] | 18,641 | https://openreview.net/forum?id=RVrINT6MT7 |
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