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[] | Poster | [] | Saliency-based representation visualization (SRV) ($e.g.$, Grad-CAM) is one of the most classical and widely adopted explainable artificial intelligence (XAI) methods for its simplicity and efficiency. It can be used to interpret deep neural networks by locating saliency areas contributing the most to their predictions. However, it is difficult to automatically measure and evaluate the performance of SRV methods due to the lack of ground-truth salience areas of samples. In this paper, we revisit the backdoor-based SRV evaluation, which is currently the only feasible method to alleviate the previous problem. We first reveal its \emph{implementation limitations} and \emph{unreliable nature} due to the trigger generalization of existing backdoor watermarks. Given these findings, we propose a generalization-limited backdoor watermark (GLBW), based on which we design a more faithful XAI evaluation. Specifically, we formulate the training of watermarked DNNs as a min-max problem, where we find the `worst' potential trigger (with the highest attack effectiveness and differences from the ground-truth trigger) via inner maximization and minimize its effects and the loss over benign and poisoned samples via outer minimization in each iteration. In particular, we design an adaptive optimization method to find desired potential triggers in each inner maximization. Extensive experiments on benchmark datasets are conducted, verifying the effectiveness of our generalization-limited watermark. | [] | [] | Towards Faithful XAI Evaluation via Generalization-Limited Backdoor Watermark | [
"Mengxi Ya",
"Yiming Li",
"Tao Dai",
"Bin Wang",
"Yong Jiang",
"Shu-Tao Xia"
] | 18,298 | https://openreview.net/forum?id=cObFETcoeW |
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[] | Poster | [] | We present chain-of-knowledge (CoK), a novel framework that augments large language models (LLMs) by dynamically incorporating grounding information from heterogeneous sources. It results in more factual rationales and reduced hallucination in generation. Specifically, CoK consists of three stages: reasoning preparation, dynamic knowledge adapting, and answer consolidation. Given a knowledge-intensive question, CoK first prepares several preliminary rationales and answers while identifying the relevant knowledge domains.If there is no majority consensus among the answers from samples, CoK corrects the rationales step by step by adapting knowledge from the identified domains.These corrected rationales can plausibly serve as a better foundation for the final answer consolidation.Unlike prior studies that primarily use unstructured data, CoK also leverages structured knowledge sources such as Wikidata and tables that provide more reliable factual information.To access both unstructured and structured knowledge sources in the dynamic knowledge adapting stage, we propose an adaptive query generator that allows the generation of queries for various types of query languages, including SPARQL, SQL, and natural sentences. Moreover, to minimize error propagation between rationales, CoK corrects the rationales progressively using preceding corrected rationales to generate and correct subsequent rationales.Extensive experiments show that CoK consistently improves the performance of LLMs on knowledge-intensive tasks across different domains. | [] | [] | Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources | [
"Xingxuan Li",
"Ruochen Zhao",
"Yew Ken Chia",
"Bosheng Ding",
"Shafiq Joty",
"Soujanya Poria",
"Lidong Bing"
] | 18,297 | https://openreview.net/forum?id=cPgh4gWZlz |
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[] | Poster | [] | Top-K sparse softmax gating mixture of experts has been widely used for scaling up massive deep-learning architectures without increasing the computational cost. Despite its popularity in real-world applications, the theoretical understanding of that gating function has remained an open problem. The main challenge comes from the structure of the top-K sparse softmax gating function, which partitions the input space into multiple regions with distinct behaviors. By focusing on a Gaussian mixture of experts, we establish theoretical results on the effects of the top-K sparse softmax gating function on both density and parameter estimations. Our results hinge upon defining novel loss functions among parameters to capture different behaviors of the input regions. When the true number of experts $k_{\ast}$ is known, we demonstrate that the convergence rates of density and parameter estimations are both parametric on the sample size. However, when $k_{\ast}$ becomes unknown and the true model is over-specified by a Gaussian mixture of $k$ experts where $k > k_{\ast}$, our findings suggest that the number of experts selected from the top-K sparse softmax gating function must exceed the total cardinality of a certain number of Voronoi cells associated with the true parameters to guarantee the convergence of the density estimation. Moreover, while the density estimation rate remains parametric under this setting, the parameter estimation rates become substantially slow due to an intrinsic interaction between the softmax gating and expert functions. | [] | [] | Statistical Perspective of Top-K Sparse Softmax Gating Mixture of Experts | [
"Huy Nguyen",
"Pedram Akbarian",
"Fanqi Yan",
"Nhat Ho"
] | 18,000 | https://openreview.net/forum?id=jvtmdK69KQ |
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[] | Poster | [] | In critical applications, it is vital for classifiers to defer decision-making to humans. We propose a post-hoc method that makes existing classifiers selectively abstain from predicting certain samples. Our abstaining classifier is incentivized to maintain the original accuracy for each sub-population (i.e. no harm) while achieving a set of group fairness definitions to a user specified degree. To this end, we design an Integer Programming (IP) procedure that assigns abstention decisions for each training sample to satisfy a set of constraints. To generalize the abstaining decisions to test samples, we then train a surrogate model to learn the abstaining decisions based on the IP solutions in an end-to-end manner. We analyze the feasibility of the IP procedure to determine the possible abstention rate for different levels of unfairness tolerance and accuracy constraint for achieving no harm. To the best of our knowledge, this work is the first to identify the theoretical relationships between the constraint parameters and the required abstention rate. Our theoretical results are important since a high abstention rate is often infeasible in practice due to a lack of human resources. Our framework outperforms existing methods in terms of fairness disparity without sacrificing accuracy at similar abstention rates. | [] | [] | Fair Classifiers that Abstain without Harm | [
"Tongxin Yin",
"Jean-Francois Ton",
"Ruocheng Guo",
"Yuanshun Yao",
"Mingyan Liu",
"Yang Liu"
] | 2310.06205 | 17,999 | https://openreview.net/forum?id=jvveGAbkVx |
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[] | Poster | [] | In deep learning, test-time adaptation has gained attention as a method for model fine-tuning without the need for labeled data. A prime exemplification is the recently proposed test-time prompt tuning for large-scale vision-language models such as CLIP. Unfortunately, these prompts have been mainly developed to improve accuracy, overlooking the importance of calibration—a crucial aspect for quantifying prediction uncertainty. However, traditional calibration methods rely on substantial amounts of labeled data, making them impractical for test-time scenarios. To this end, this paper explores calibration during test-time prompt tuning by leveraging the inherent properties of CLIP. Through a series of observations, we find that the prompt choice significantly affects the calibration in CLIP, where the prompts leading to higher text feature dispersion result in better-calibrated predictions. Introducing the Average Text Feature Dispersion (ATFD), we establish its relationship with calibration error and present a novel method, Calibrated Test-time Prompt Tuning (C-TPT), for optimizing prompts during test-time with enhanced calibration. Through extensive experiments on different CLIP architectures and datasets, we show that C-TPT can effectively improve the calibration of test-time prompt tuning without needing labeled data. The code will be publicly available. | [] | [] | C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion | [
"Hee Suk Yoon",
"Eunseop Yoon",
"Joshua Tian Jin Tee",
"Mark A. Hasegawa-Johnson",
"Yingzhen Li",
"Chang D. Yoo"
] | 17,996 | https://openreview.net/forum?id=jzzEHTBFOT |
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[] | Poster | [] | Generating 3D graphs of \textit{symmetry-group equivariance} is of intriguing potential in broad applications from machine vision to molecular discovery.Emerging approaches adopt diffusion generative models (DGMs) with proper re-engineering to capture 3D graph distributions.In this paper, we raise an orthogonal and fundamental question of \textit{in what (latent) space we should diffuse 3D graphs}.\ding{182} We motivate the study with theoretical analysis showing that the performance bound of 3D graph diffusion could be improved in a latent space versus the original space, provided that there are (i) low dimensionality yet (ii) high quality (i.e., low reconstruction error) of the latent space, and (iii) symmetry preservation as an inductive bias of latent DGMs.\ding{183} Guided by the theoretical guidelines, we propose to perform 3D graph diffusion in a low-dimensional latent space, which is learned through cascaded 2D--3D graph autoencoders for low-error reconstruction and symmetry-group invariance.The overall pipeline is dubbed \textbf{latent 3D graph diffusion}.\ding{184} Motivated by applications in molecular discovery, we further extend latent 3D graph diffusion to conditional generation given SE(3)-invariant attributes or equivariant 3D objects.\ding{185} We also demonstrate empirically that out-of-distribution conditional generation can be further improved by regularizing the latent space via graph self-supervised learning.We validate through comprehensive experiments that our method generates 3D molecules of higher validity / drug-likeliness and comparable conformations / energetics, while being an order of magnitude faster in training. Codes will be released upon acceptance. | [] | [] | Latent 3D Graph Diffusion | [
"Yuning You",
"Ruida Zhou",
"Jiwoong Park",
"Haotian Xu",
"Chao Tian",
"Zhangyang Wang",
"Yang Shen"
] | 18,292 | https://openreview.net/forum?id=cXbnGtO0NZ |
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[] | Spotlight Poster | [] | Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR), which leverages the rich linguistic knowledge and powerful reasoning ability of LLMs to improve recognition results. The latest work proposes a GER benchmark with "HyPoradise" dataset to learn the mapping from ASR N-best hypotheses to ground-truth transcription by efficient LLM finetuning, which shows great effectiveness but lacks specificity on noise-robust ASR. In this work, we extend the benchmark to noisy conditions and investigate if we can teach LLMs to perform denoising for GER just like what robust ASR do, where one solution is introducing noise information as a conditioner into LLM. However, directly incorporating noise embeddings from audio encoder could harm the LLM tuning due to cross-modality gap. To this end, we propose to extract a language-space noise embedding from the N-best list to represent the noise conditions of source speech, which can promote the denoising process in GER. Furthermore, in order to enhance its representation ability of audio noise, we design a knowledge distillation (KD) approach via mutual information estimation to distill the real noise information in audio embeddings to our language embedding. Experiments on various latest LLMs demonstrate our approach achieves a new breakthrough with up to 53.9% correction improvement in terms of word error rate while with limited training data. Analysis shows that our language-space noise embedding can well represent the noise conditions of source speech, under which off-the-shelf LLMs show strong ability of language-space denoising. | [] | [] | Large Language Models are Efficient Learners of Noise-Robust Speech Recognition | [
"Yuchen Hu",
"CHEN CHEN",
"Chao-Han Huck Yang",
"Ruizhe Li",
"Chao Zhang",
"Pin-Yu Chen",
"Ensiong Chng"
] | 2401.10446 | 18,285 | https://openreview.net/forum?id=ceATjGPTUD |
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[] | Spotlight Poster | [] | We train small transformers to calculate the greatest common divisor (GCD) of two positive integers, and show that their predictions are fully explainable. During training, models learn a list $\mathcal D$ of divisors, and predict the largest element of $\mathcal D$ that divides both inputs. We also show that training distributions have a large impact on performance. Models trained from uniform operands only learn a handful of GCD (up to $38$ out of $100$). Training from log-uniform operands boosts performance to $73$ correct GCD, and balancing the distribution of GCD, from inverse square to log-uniform, to $91$. On the other hand, a uniform distribution of GCD in the training set breaks model explainability. | [] | [] | Learning the greatest common divisor: explaining transformer predictions | [
"Francois Charton"
] | 2308.15594 | 18,284 | https://openreview.net/forum?id=cmcD05NPKa |
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[] | Poster | [] | Spiking Neural Networks (SNNs) are attracting growing interest for their energy-efficient computing when implemented on neuromorphic hardware. However, directly training SNNs, even adopting batch normalization (BN), is highly challenging due to their non-differentiable activation function and the temporally delayed accumulation of outputs over time. For SNN training, this temporal accumulation gives rise to Temporal Covariate Shifts (TCS) along the temporal dimension, a phenomenon that would become increasingly pronounced with layerwise computations across multiple layers and time-steps. In this paper, we introduce TAB (Temporal Accumulated Batch Normalization), a novel SNN batch normalization method that addresses the temporal covariate shift issue by aligning with neuron dynamics (specifically the accumulated membrane potential) and utilizing temporal accumulated statistics for data normalization. Within its framework, TAB effectively encapsulates the historical temporal dependencies that underlie the membrane potential accumulation process, thereby establishing a natural connection between neuron dynamics and TAB batch normalization. Experimental results on CIFAR-10, CIFAR-100, and DVS-CIFAR10 show that our TAB method outperforms other state-of-the-art methods. | [] | [] | TAB: Temporal Accumulated Batch Normalization in Spiking Neural Networks | [
"Haiyan Jiang",
"Vincent Zoonekynd",
"Giulia De Masi",
"Bin Gu",
"Huan Xiong"
] | 17,995 | https://openreview.net/forum?id=k1wlmtPGLq |
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[] | Poster | [] | Pre-trained transformers are often fine-tuned to aid clinical decision-making using limited clinical notes. Model interpretability is crucial, especially in high-stakes domains like medicine, to establish trust and ensure safety, which requires human engagement. We introduce SUFO, a systematic framework that enhances interpretability of fine-tuned transformer feature spaces. SUFO utilizes a range of analytic and visualization techniques, including Supervised probing, Unsupervised similarity analysis, Feature dynamics, and Outlier analysis to address key questions about model trust and interpretability (e.g. model suitability for a task, feature space evolution during fine-tuning, and interpretation of fine-tuned features and failure modes). We conduct a case study investigating the impact of pre-training data where we focus on real-world pathology classification tasks, and validate our findings on MedNLI. We evaluate five 110M-sized pre-trained transformer models, categorized into general-domain (BERT, TNLR), mixed-domain (BioBERT, Clinical BioBERT), and domain-specific (PubMedBERT) groups. Our SUFO analyses reveal that: (1) while PubMedBERT, the domain-specific model, contains valuable information for fine-tuning, it can overfit to minority classes when class imbalances exist. In contrast, mixed-domain models exhibit greater resistance to overfitting, suggesting potential improvements in domain-specific model robustness; (2) in-domain pre-training accelerates feature disambiguation during fine-tuning; and (3) feature spaces undergo significant sparsification during this process, enabling clinicians to identify common outlier modes among fine-tuned models as demonstrated in this paper. These findings showcase the utility of SUFO in enhancing trust and safety when using transformers in medicine, and we believe SUFO can aid practitioners in evaluating fine-tuned language models (LMs) for other applications in medicine and in more critical domains. | [] | [] | Diagnosing Transformers: Illuminating Feature Spaces for Clinical Decision-Making | [
"Aliyah R. Hsu",
"Yeshwanth Cherapanamjeri",
"Briton Park",
"Tristan Naumann",
"Anobel Odisho",
"Bin Yu"
] | 2305.17588 | 17,994 | https://openreview.net/forum?id=k581sTMyPt |
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[] | Poster | [] | With the rising tide of large language models (LLMs), there has been a growing interest in developing general-purpose instruction-following models, e.g., ChatGPT. To this end, we present LLaMA-Adapter, a lightweight adaption method for efficient instruction tuning of LLaMA. Using 52K self-instruct demonstrations, LLaMA-Adapter only introduces 1.2M learnable parameters upon the frozen LLaMA 7B model, and costs less than one hour for fine-tuning. Specifically, a zero-initialized attention mechanism is proposed. It adopts a learnable zero gating to adaptively inject the instructional cues into LLaMA within self-attention layers, contributing to a stable training process and superior final performance. In this way, LLaMA-Adapter can generate high-quality responses to diverse language instructions, comparable to Alpaca with fully fine-tuned 7B parameters. Besides language commands, by incorporating an image encoder, our approach can be simply extended to a multi-modal LLM for image-conditioned instruction following, which achieves superior multi-modal reasoning capacity on several popular benchmarks (MME, MMBench, LVLM-eHub). Furthermore, we also verify the proposed zero-initialized attention mechanism for fine-tuning other pre-trained models (ViT, RoBERTa, CLIP) on traditional vision and language tasks, demonstrating the effectiveness and generalizability of our approach. | [] | [] | LLaMA-Adapter: Efficient Fine-tuning of Large Language Models with Zero-initialized Attention | [
"Renrui Zhang",
"Jiaming Han",
"Chris Liu",
"Aojun Zhou",
"Pan Lu",
"Hongsheng Li",
"Peng Gao",
"Yu Qiao"
] | 18,277 | https://openreview.net/forum?id=d4UiXAHN2W |
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[] | Poster | [] | Multimodal VAEs have recently gained significant attention as generative models for weakly-supervised learning with multiple heterogeneous modalities. In parallel, VAE-based methods have been explored as probabilistic approaches for clustering tasks. At the intersection of these two research directions, we propose a novel multimodal VAE model in which the latent space is extended to learn data clusters, leveraging shared information across modalities. Our experiments show that our proposed model improves generative performance over existing multimodal VAEs, particularly for unconditional generation. Furthermore, we propose a post-hoc procedure to automatically select the number of true clusters thus mitigating critical limitations of previous clustering frameworks. Notably, our method favorably compares to alternative clustering approaches, in weakly-supervised settings. Finally, we integrate recent advancements in diffusion models into the proposed method to improve generative quality for real-world images. | [] | [] | Deep Generative Clustering with Multimodal Diffusion Variational Autoencoders | [
"Emanuele Palumbo",
"Laura Manduchi",
"Sonia Laguna",
"Daphné Chopard",
"Julia E Vogt"
] | 17,993 | https://openreview.net/forum?id=k5THrhXDV3 |
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[] | Poster | [] | The utility of a learned neural representation depends on how well its geometry supports performance in downstream tasks. This geometry depends on the structure of the inputs, the structure of the target outputs, and on the architecture of the network. By studying the learning dynamics of networks with one hidden layer, we discovered that the network's activation function has an unexpectedly strong impact on the representational geometry: Tanh networks tend to learn representations that reflect the structure of the target outputs, while ReLU networks retain more information about the structure of the raw inputs. This difference is consistently observed across a broad class of parameterized tasks in which we modulated the degree of alignment between the geometry of the task inputs and that of the task labels. We analyzed the learning dynamics in weight space and show how the differences between the networks with Tanh and ReLU nonlinearities arise from the asymmetric saturation of ReLU, which leads feature neurons to specialize for different regions of input space. Feature neurons in Tanh networks, by contrast, tend to inherit the task label structure. Consequently, when the target outputs are low dimensional, Tanh networks generate neural representations that are more disentangled than those obtained with a ReLU nonlinearity. Our findings shed light on the interplay between input-output geometry, nonlinearity, and learned representations in neural networks. | [] | [] | Task structure and nonlinearity jointly determine learned representational geometry | [
"Matteo Alleman",
"Jack Lindsey",
"Stefano Fusi"
] | 2401.13558 | 17,991 | https://openreview.net/forum?id=k9t8dQ30kU |
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[] | Poster | [] | Procedural planning, which entails decomposing a high-level goal into a sequence of temporally ordered steps, is an important yet intricate task for machines. It involves integrating common-sense knowledge to reason about complex and often contextualized situations, e.g. ``scheduling a doctor's appointment without a phone''. While current approaches show encouraging results using large language models (LLMs), they are hindered by drawbacks such as costly API calls and reproducibility issues. In this paper, we advocate planning using smaller language models. We present PlaSma, a novel two-pronged approach to endow small language models with procedural knowledge and (constrained) language-based planning capabilities. More concretely, we develop *symbolic procedural knowledge distillation* to enhance the commonsense knowledge in small language models and an*inference-time algorithm* to facilitate more structured and accurate reasoning. In addition, we introduce a new related task, *Replanning*, that requires a revision of a plan to cope with a constrained situation. In both the planning and replanning settings, we show that orders-of-magnitude smaller models (770M-11B parameters) can compete and often surpass their larger teacher models' capabilities. Finally, we showcase successful application of PlaSma in an embodied environment, VirtualHome. | [] | [] | PlaSma: Procedural Knowledge Models for Language-based Planning and Re-Planning | [
"Faeze Brahman",
"Chandra Bhagavatula",
"Valentina Pyatkin",
"Jena D. Hwang",
"Xiang Lorraine Li",
"Hirona Jacqueline Arai",
"Soumya Sanyal",
"Keisuke Sakaguchi",
"Xiang Ren",
"Yejin Choi"
] | 18,269 | https://openreview.net/forum?id=dFcXJgnrGB |
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[] | Poster | [
"https://github.com/NVlabs/FasterViT"
] | We design a new family of hybrid CNN-ViT neural networks, named FasterViT, with a focus on high image throughput for computer vision (CV) applications. FasterViT combines the benefits of fast local representation learning in CNNs and global modeling properties in ViT. Our newly introduced Hierarchical Attention (HAT) approach decomposes global self-attention with quadratic complexity into a multi-level attention with reduced computational costs. We benefit from efficient window-based self-attention. Each window has access to dedicated carrier tokens that participate in local and global representation learning. At a high level, global self-attentions enable the efficient cross-window communication at lower costs. FasterViT achieves a SOTA Pareto-front in terms of accuracy \vs image throughput. We have extensively validated its effectiveness on various CV tasks including classification, object detection and segmentation. We also show that HAT can be used as a plug-and-play module for existing networks and enhance them. We further demonstrate significantly faster and more accurate performance than competitive counterparts for images with high resolution. Code is available at https://bit.ly/FasterViT. | [] | [] | FasterViT: Fast Vision Transformers with Hierarchical Attention | [
"Ali Hatamizadeh",
"Greg Heinrich",
"Hongxu Yin",
"Andrew Tao",
"Jose M. Alvarez",
"Jan Kautz",
"Pavlo Molchanov"
] | 2306.06189 | 17,990 | https://openreview.net/forum?id=kB4yBiNmXX |
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[] | Poster | [] | Normalizing Flows explicitly maximize a full-dimensional likelihood on the training data. However, real data is typically only supported on a lower-dimensional manifold leading the model to expend significant compute on modeling noise. Injective Flows fix this by jointly learning a manifold and the distribution on it. So far, they have been limited by restrictive architectures and/or high computational cost. We lift both constraints by a new efficient estimator for the maximum likelihood loss, compatible with free-form bottleneck architectures. We further show that naively learning both the data manifold and the distribution on it can lead to divergent solutions, and use this insight to motivate a stable maximum likelihood training objective. We perform extensive experiments on toy, tabular and image data, demonstrating the competitive performance of the resulting model. | [] | [] | Lifting Architectural Constraints of Injective Flows | [
"Peter Sorrenson",
"Felix Draxler",
"Armand Rousselot",
"Sander Hummerich",
"Lea Zimmermann",
"Ullrich Koethe"
] | 2306.01843 | 17,989 | https://openreview.net/forum?id=kBNIx4Biq4 |
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[] | Poster | [] | There is increasing interest in methods for extracting interpretable rules from ML models trained to solve a wide range of tasks over knowledge graphs (KGs), such as KG completion, node classification, question answering and recommendation. Many such approaches, however, lack formal guarantees establishing the precise relationship between the model and the extracted rules, and this lack of assurance becomes especially problematic when the extracted rules are applied in safety-critical contexts or to ensure compliance with legal requirements. Recent research has examined whether the rules derived from the influential Neural-LP model exhibit soundness (or completeness), which means that the results obtained by applying the model to any dataset always contain (or are contained in) the results obtained by applying the rules to the same dataset. In this paper, we extend this analysis to the context of DRUM, an approach that has demonstrated superior practical performance. After observing that the rules currently extracted from a DRUM model can be unsound and/or incomplete, we propose a novel algorithm where the output rules, expressed in an extension of Datalog, ensure both soundness and completeness. This algorithm, however, can be inefficient in practice and hence we propose additional constraints to DRUM models facilitating rule extraction, albeit at the expense of reduced expressive power. | [] | [] | Faithful Rule Extraction for Differentiable Rule Learning Models | [
"Xiaxia Wang",
"David Jaime Tena Cucala",
"Bernardo Cuenca Grau",
"Ian Horrocks"
] | 17,988 | https://openreview.net/forum?id=kBTzlxM2J1 |
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[] | Poster | [] | Recent work has demonstrated the significant potential of denoising diffusion modelsfor generating human motion, including text-to-motion capabilities.However, these methods are restricted by the paucity of annotated motion data,a focus on single-person motions, and a lack of detailed control.In this paper, we introduce three forms of composition based on diffusion priors:sequential, parallel, and model composition.Using sequential composition, we tackle the challenge of long sequencegeneration. We introduce DoubleTake, an inference-time method with whichwe generate long animations consisting of sequences of prompted intervalsand their transitions, using a prior trained only for short clips.Using parallel composition, we show promising steps toward two-person generation.Beginning with two fixed priors as well as a few two-person training examples, we learn a slimcommunication block, ComMDM, to coordinate interaction between the two resulting motions.Lastly, using model composition, we first train individual priorsto complete motions that realize a prescribed motion for a given joint.We then introduce DiffusionBlending, an interpolation mechanism to effectively blend severalsuch models to enable flexible and efficient fine-grained joint and trajectory-level control and editing.We evaluate the composition methods using an off-the-shelf motion diffusion model,and further compare the results to dedicated models trained for these specific tasks. | [] | [] | Human Motion Diffusion as a Generative Prior | [
"Yoni Shafir",
"Guy Tevet",
"Roy Kapon",
"Amit Haim Bermano"
] | 2303.01418 | 18,256 | https://openreview.net/forum?id=dTpbEdN9kr |
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[] | Poster | [] | Given the success of Large Language Models (LLMs), there has been considerable interest in studying the properties of model activations. The literature overwhelmingly agrees that LLM representations are dominated by a few ``outlier dimensions'' with exceedingly high variance and magnitude. Several studies in Natural Language Processing (NLP) have sought to mitigate the impact of such outlier dimensions and force LLMs to be isotropic (i.e., have uniform variance across all dimensions in embedding space). Isotropy is thought to be a desirable property for LLMs that improves model performance and more closely aligns textual representations with human intuition. However, many claims regarding isotropy in NLP have been based on the average cosine similarity of embeddings, which has recently been shown to be a flawed measure of isotropy. In this paper, we propose I-STAR: IsoScore$^{\star}$-based STable Anisotropic Regularization, a novel regularization method that can be used to increase or decrease levels of isotropy in embedding space during training. I-STAR uses IsoScore$^{\star}$, the first accurate measure of isotropy that is both differentiable and stable on mini-batch computations. In contrast to several previous works, we find that \textit{decreasing} isotropy in contextualized embeddings improves performance on the majority of tasks and models considered in this paper. | [] | [] | Stable Anisotropic Regularization | [
"William Rudman",
"Carsten Eickhoff"
] | 2305.19358 | 18,254 | https://openreview.net/forum?id=dbQH9AOVd5 |
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[] | Poster | [
"https://github.com/mzhaoshuai/RLCF"
] | One fascinating aspect of pre-trained vision-language models (VLMs) learning under language supervision is their impressive zero-shot generalization capability.However, this ability is hindered by distribution shifts between the training and testing data.Previous test time adaptation (TTA) methods for VLMs in zero-shot classification rely on minimizing the entropy of model outputs, tending to be stuck in incorrect model predictions.In this work, we propose TTA with feedback to rectify the model output and prevent the model from becoming blindly confident.Specifically, a CLIP model is adopted as the reward model during TTA and provides feedback for the VLM.Given a single test sample,the VLM is forced to maximize the CLIP reward between the input and sampled results from the VLM output distribution.The proposed \textit{reinforcement learning with CLIP feedback~(RLCF)} framework is highly flexible and universal.Beyond the classification task, with task-specific sampling strategies and a proper reward baseline choice, RLCF can be easily extended to not only discrimination tasks like retrieval but also generalization tasks like image captioning,improving the zero-shot generalization capacity of VLMs.According to the characteristics of these VL tasks, we build different fully TTA pipelines with RLCF to improve the zero-shot generalization ability of various VLMs.Extensive experiments along with promisingempirical results demonstrate the effectiveness of RLCF.The code is available at https://github.com/mzhaoshuai/RLCF. | [] | [] | Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models | [
"Shuai Zhao",
"Xiaohan Wang",
"Linchao Zhu",
"Yi Yang"
] | 2305.18010 | 17,984 | https://openreview.net/forum?id=kIP0duasBb |
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[] | Poster | [] | Diffusion-based image editing methods have achieved remarkable advances in text-driven image editing. The editing task aims to edit an input image with the original prompt to the desired image aligned with the target prompt. By comparing the original and target prompts, we can obtain numerous editing pairs, each comprising an object and its corresponding editing target. To allow editability while maintaining fidelity to the input image, existing editing methods typically involve a fixed number of inversion steps that project the whole input image to its noisier latent representation, followed by a denoising process guided by the target prompt. However, we find that the optimal number of inversion steps for achieving ideal editing results varies significantly among different editing pairs, owing to varying editing difficulties. Therefore, the current literature, which relies on a fixed number of inversion steps, produces suboptimal generation quality, especially when handling multiple editing pairs in a natural image.To this end, we propose a new image editing paradigm, dubbed Object-aware Inversion and Reassembly (OIR), to enable object-level fine-grained editing. Specifically, we design a new search metric, which determines the optimal inversion step for each editing pair, by jointly considering the editability of the target and the fidelity of the non-editing region. When editing an image, we first search the optimal inversion step for each editing pair with our search metric and edit them separately to circumvent concept mismatch. Subsequently, we propose an additional reassembly step to seamlessly integrate the respective editing results and the non-editing region to obtain the final edited image. To systematically evaluate the effectiveness of our method, we collect two datasets for benchmarking single- and multi-object editing, respectively. Experiments demonstrate that our method achieves superior performance in editing object shapes, colors, materials, categories, etc., especially in multi-object editing scenarios. | [] | [] | Object-Aware Inversion and Reassembly for Image Editing | [
"Zhen Yang",
"Ganggui Ding",
"Wen Wang",
"Hao Chen",
"Bohan Zhuang",
"Chunhua Shen"
] | 2310.12149 | 18,242 | https://openreview.net/forum?id=dpcVXiMlcv |
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[] | Poster | [] | The efficacy of modern generative models is commonly contingent upon the precision of score estimation along the diffusion path, with a focus on diffusion models and their ability to generate high-quality data samples. This study delves into the application of reverse diffusion to Monte Carlo sampling. It is shown that score estimation can be transformed into a mean estimation problem via the decomposition of the transition kernel. By estimating the mean of the posterior distribution, we derive a novel Monte Carlo sampling algorithm from the reverse diffusion process, which is distinct from traditional Markov Chain Monte Carlo (MCMC) methods. We calculate the error requirements and sample size for the posterior distribution, and use the result to derive an algorithm that can approximate the target distribution to any desired accuracy. Additionally, by estimating the log-Sobolev constant of the posterior distribution, we show under suitable conditions the problem of sampling from the posterior can be easier than direct sampling from the target distribution using traditional MCMC techniques. For Gaussian mixture models, we demonstrate that the new algorithm achieves significant improvement over the traditional Langevin-style MCMC sampling methods both theoretically and practically. Our algorithm offers a new perspective and solution beyond classical MCMC algorithms for challenging complex distributions. | [] | [] | Reverse Diffusion Monte Carlo | [
"Xunpeng Huang",
"Hanze Dong",
"Yifan HAO",
"Yian Ma",
"Tong Zhang"
] | 2307.02037 | 17,983 | https://openreview.net/forum?id=kIPEyMSdFV |
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[] | Poster | [] | Controllable layout generation refers to the process of creating a plausible visual arrangement of elements within a graphic design (*e.g.*, document and web designs) with constraints representing design intentions. Although recent diffusion-based models have achieved state-of-the-art FID scores, they tend to exhibit more pronounced misalignment compared to earlier transformer-based models. In this work, we propose the **LA**yout **C**onstraint diffusion mod**E**l (LACE), a unified model to handle a broad range of layout generation tasks, such as arranging elements with specified attributes and refining or completing a coarse layout design. The model is based on continuous diffusion models. Compared with existing methods that use discrete diffusion models, continuous state-space design can enable the incorporation of continuous aesthetic constraint functions in training more naturally. For conditional generation, we propose injecting layout conditions in the form of masks or gradient guidance during inference. Empirical results show that LACE produces high-quality layouts and outperforms existing state-of-the-art baselines. We will release our source code and model checkpoints. | [] | [] | Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints | [
"Jian Chen",
"Ruiyi Zhang",
"Yufan Zhou",
"Changyou Chen"
] | 2402.04754 | 17,981 | https://openreview.net/forum?id=kJ0qp9Xdsh |
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[] | Poster | [] | Coordinate network or implicit neural representation (INR) is a fast-emerging method for encoding natural signals (such as images and videos) with the benefits of a compact neural representation. While numerous methods have been proposed to increase the encoding capabilities of an INR, an often overlooked aspect is the inference efficiency, usually measured in multiply-accumulate (MAC) count. This is particularly critical in use cases where inference bandwidth is greatly limited by hardware constraints. To this end, we propose the Activation-Sharing Multi-Resolution (ASMR) coordinate network that combines multi-resolution coordinate decomposition with hierarchical modulations. Specifically, an ASMR model enables the sharing of activations across grids of the data. This largely decouples its inference cost from its depth which is directly correlated to its reconstruction capability, and renders a near $O(1)$ inference complexity irrespective of the number of layers. Experiments show that ASMR can reduce the MAC of a vanilla SIREN model by up to 350$\times$ while achieving an even higher reconstruction quality than its SIREN baseline. | [] | [] | ASMR: Activation-Sharing Multi-Resolution Coordinate Networks for Efficient Inference | [
"Jason Chun Lok Li",
"Steven Tin Sui Luo",
"Le Xu",
"Ngai Wong"
] | 17,979 | https://openreview.net/forum?id=kMp8zCsXNb |
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[] | Poster | [] | Proximal operators are ubiquitous in inverse problems, commonly appearing as part of algorithmic strategies to regularize problems that are otherwise ill-posed. Modern deep learning models have been brought to bear for these tasks too, as in the framework of plug-and-play or deep unrolling, where they loosely resemble proximal operators. Yet, these do not provide any guarantee that these general functions, implemented by neural networks, provide a proximal operator of some function, nor do they provide any characterization of the function of which they provide some approximate proximal. Herein we provide a framework to develop learned proximal networks (LPN), which provide exact proximal operators for a data-driven regularizer, and show how a new training strategy, dubbed proximal matching, guarantees that the obtained regularizer recovers the log-prior of the true data distribution. Thus, such LPN provide general, unsupervised, proximal operators that can be used for general inverse problems. We illustrate our results in a series of cases of increasing complexity. demonstrating that these models not only result in state-of-the-art restoration results, but provide a window into the resulting priors learned from data. | [] | [] | What's in a Prior? Learned Proximal Networks for Inverse Problems | [
"Zhenghan Fang",
"Sam Buchanan",
"Jeremias Sulam"
] | 2310.14344 | 17,978 | https://openreview.net/forum?id=kNPcOaqC5r |
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[] | Poster | [] | Dynamic Sparse Training (DST) methods achieve state-of-the-art results in sparse neural network training, matching the generalization of dense models while enabling sparse training and inference. Although the resulting models are highly sparse and theoretically less computationally expensive, achieving speedups with unstructured sparsity on real-world hardware is challenging. In this work, we propose a sparse-to-sparse DST method, Structured RigL (SRigL), to learn a variant of fine-grained structured N:M sparsity by imposing a constant fan-in constraint. Using our empirical analysis of existing DST methods at high sparsity, we additionally employ a neuron ablation method which enables SRigL to achieve state-of-the-art sparse-to-sparse structured DST performance on a variety of Neural Network (NN) architectures. Using a 90% sparse linear layer, we demonstrate a real-world acceleration of 3.4×/2.5× on CPU for online inference and 1.7×/13.0× on GPU for inference with a batch size of 256 when compared to equivalent dense/unstructured (CSR) sparse layers, respectively. | [] | [] | Dynamic Sparse Training with Structured Sparsity | [
"Mike Lasby",
"Anna Golubeva",
"Utku Evci",
"Mihai Nica",
"Yani Ioannou"
] | 2305.02299 | 17,975 | https://openreview.net/forum?id=kOBkxFRKTA |
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[] | Poster | [] | Neural networks have shown promising performance in collaborative filtering and matrix completion but the theoretical analysis is limited and there is still room for improvement in terms of the accuracy of recovering missing values. This paper presents a neuron-enhanced autoencoder matrix completion (AEMC-NE) method and applies it to collaborative filtering. Our AEMC-NE adds an element-wise autoencoder to each output of the main autoencoder to enhance the reconstruction capability. Thus it can adaptively learn an activation function for the output layer to approximate possibly complicated response functions in real data. We provide theoretical analysis for AEMC-NE as well as AEMC to investigate the generalization ability of autoencoder and deep learning in matrix completion, considering both missing completely at random and missing not at random. We show that the element-wise neural network has the potential to reduce the generalization error bound, the data sparsity can be useful, and the prediction performance is closely related to the difference between the numbers of variables and samples. The numerical results on synthetic data and benchmark datasets demonstrated the effectiveness of AEMC-NE in comparison to many baselines. | [] | [] | Neuron-Enhanced AutoEncoder Matrix Completion and Collaborative Filtering: Theory and Practice | [
"Jicong Fan",
"Rui Chen",
"Zhao Zhang",
"Chris Ding"
] | 17,974 | https://openreview.net/forum?id=kPrxk6tUcg |
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[] | Poster | [] | In the face of the deep learning model's vulnerability to domain shift, source-free domain adaptation (SFDA) methods have been proposed to adapt models to new, unseen target domains without requiring access to source domain data. Although the potential benefits of applying data augmentation to SFDA are attractive, several challenges arise such as the dependence on prior knowledge of class-preserving transformations and the increase in memory and computational requirements. In this paper, we propose Source-free Domain Adaptation Through the Lens of Data Augmentation (SF(DA)$^2$), a novel approach that leverages the benefits of data augmentation without suffering from these challenges. We construct an augmentation graph in the feature space of the pretrained model using the neighbor relationships between target features and propose spectral neighborhood clustering to identify partitions in the prediction space. Furthermore, we propose implicit feature augmentation and feature disentanglement as regularization loss functions that effectively utilize class semantic information within the feature space. These regularizers simulate the inclusion of an unlimited number of augmented target features into the augmentation graph while minimizing computational and memory demands. Our method shows superior adaptation performance in SFDA scenarios, including 2D image and 3D point cloud datasets and a highly imbalanced dataset. | [] | [] | SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation | [
"Uiwon Hwang",
"Jonghyun Lee",
"Juhyeon Shin",
"Sungroh Yoon"
] | 2403.10834 | 17,973 | https://openreview.net/forum?id=kUCgHbmO11 |
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[] | Spotlight Poster | [] | Existing Self-Supervised Learning (SSL) models for speech typically process speech signals at a fixed resolution of 20 milliseconds. This approach overlooks the varying informational content present at different resolutions in speech signals. In contrast, this paper aims to incorporate multi-resolution information into speech self-supervised representation learning. We introduce a SSL model that leverages a hierarchical Transformer architecture, complemented by HuBERT-style masked prediction objectives, to process speech at multiple resolutions. Experimental results indicate that the proposed model not only achieves more efficient inference but also exhibits superior or comparable performance to the original HuBERT model over various tasks. Specifically, significant performance improvements over the original HuBERT have been observed in fine-tuning experiments on the LibriSpeech speech recognition benchmark as well as in evaluations using the Speech Universal PERformance Benchmark (SUPERB) and Multilingual SUPERB (ML-SUPERB). | [] | [] | Multi-resolution HuBERT: Multi-resolution Speech Self-Supervised Learning with Masked Unit Prediction | [
"Jiatong Shi",
"Hirofumi Inaguma",
"Xutai Ma",
"Ilia Kulikov",
"Anna Sun"
] | 2310.02720 | 17,972 | https://openreview.net/forum?id=kUuKFW7DIF |
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[] | Poster | [] | Symmetry learning has proven to be an effective approach for extracting the hidden structure of data, with the concept of equivariance relation playing the central role. However, most of the current studies are built on architectural theory and corresponding assumptions on the form of data. We propose Neural Fourier Transform (NFT), a general framework of learning the latent linear action of the group without assuming explicit knowledge of how the group acts on data.We present the theoretical foundations of NFT and show that the existence of a linear equivariant feature, which has been assumed ubiquitously in equivariance learning, is equivalent to the existence of a group invariant kernel on the dataspace. We also provide experimental results to demonstrate the application of NFT in typical scenarios with varying levels of knowledge about the acting group. | [] | [] | Neural Fourier Transform: A General Approach to Equivariant Representation Learning | [
"Masanori Koyama",
"Kenji Fukumizu",
"Kohei Hayashi",
"Takeru Miyato"
] | 2305.18484 | 18,222 | https://openreview.net/forum?id=eOCvA8iwXH |
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[] | Poster | [] | Diffusion-based generative methods have proven effective in modeling trajectories with offline datasets. However, they often face computational challenges and can falter in generalization, especially in capturing temporal abstractions for long-horizon tasks. To overcome this, we introduce the Hierarchical Diffuser, a simple, fast, yet effective planning method combining the advantages of hierarchical and diffusion-based planning. Our model adopts a “jumpy” planning strategy at the high level, which allows it to have a larger receptive field but at a lower computational cost—a crucial factor for diffusion-based planning methods, as we have empirically verified. Additionally, the jumpy sub-goals guide our low-level planner, facilitating a fine-tuning stage and further improving our approach’s effectiveness. We conducted empirical evaluations on standard offline reinforcement learning benchmarks, demonstrating our method’s superior performance and efficiency in terms of training and planning speed compared to the non-hierarchical Diffuser as well as other hierarchical planning methods. Moreover, we explore our model’s generalization capability, particularly on how our method improves generalization capabilities on compositional out-of-distribution tasks. | [] | [] | Simple Hierarchical Planning with Diffusion | [
"Chang Chen",
"Fei Deng",
"Kenji Kawaguchi",
"Caglar Gulcehre",
"Sungjin Ahn"
] | 2401.02644 | 17,970 | https://openreview.net/forum?id=kXHEBK9uAY |
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[] | Poster | [] | Sampling complex distributions is an important but difficult objective in various fields, including physics, chemistry, and statistics. An improvement of standard Monte Carlo (MC) methods, intensively used in particular in the context of disordered systems, is Parallel Tempering, also called replica exchange MC, in which a sequence of MC Markov chains at decreasing temperatures are run in parallel and can swap their configurations. In this work we apply the ideas of parallel tempering in the context of restricted Boltzmann machines (RBM), a paradigm of unsupervised architectures, capable to learn complex, multimodal distributions. Inspired by Deep Tempering, an approach introduced for deep belief networks, we show how to learn on top of the first RBM a stack of nested RBMs, using the representations of a RBM as ’data’ for the next one along the stack. In our Stacked Tempering approach the hidden configurations of a machine can be exchanged with the visible configurations of the next one in the stack. Replica exchanges between the different RBMs is facilitated by the increasingly clustered representations learnt by deeper RBMs, allowing for fast transitions between the different modes of the data distribution. Analytical calculations of mixing times in a simplified theoretical setting shed light on why Stacked Tempering works, and how hyperparameters, such as the aspect ratios of the RBMs and weight regularization should be chosen. We illustrate the efficiency of the Stacked Tempering method with respect to standard and replica exchange MC on several datasets: MNIST, in-silico Lattice Proteins, and the 2D-Ising model. | [] | [] | Accelerated Sampling with Stacked Restricted Boltzmann Machines | [
"Jorge Fernandez-de-Cossio-Diaz",
"Clément Roussel",
"Simona Cocco",
"Remi Monasson"
] | 17,969 | https://openreview.net/forum?id=kXNJ48Hvw1 |
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[] | Poster | [] | With the advancements in Large Language Models (LLMs), Vision-Language Models (VLMs) have reached a new level of sophistication, showing notable competence in executing intricate cognition and reasoning tasks. However, existing evaluation benchmarks, primarily relying on rigid, hand-crafted datasets to measure task-specific performance, face significant limitations in assessing the alignment of these increasingly anthropomorphic models with human intelligence. In this work, we address the limitations via Auto-Bench, which delves into exploring LLMs as proficient aligners, measuring the alignment between VLMs and human intelligence and value through automatic data curation and assessment. Specifically, for data curation, Auto-Bench utilizes LLMs (e.g., GPT-4) to automatically generate a vast set of question-answer-reasoning triplets via prompting on visual symbolic representations (e.g., captions, object locations, instance relationships, and etc. The curated data closely matches human intent, owing to the extensive world knowledge embedded in LLMs. Through this pipeline, a total of 28.5K human-verified and 3,504K unfiltered question-answer-reasoning triplets have been curated, covering 4 primary abilities and 16 sub-abilities. We subsequently engage LLMs like GPT-3.5 to serve as judges, implementing the quantitative and qualitative automated assessments to facilitate a comprehensive evaluation of VLMs. Our validation results reveal that LLMs are proficient in both evaluation data curation and model assessment, achieving an average agreement rate of 85%. We envision Auto-Bench as a flexible, scalable, and comprehensive benchmark for evaluating the evolving sophisticated VLMs. | [] | [] | Large Language Models as Automated Aligners for benchmarking Vision-Language Models | [
"Yuanfeng Ji",
"Chongjian GE",
"Weikai Kong",
"Enze Xie",
"Zhengying Liu",
"Zhenguo Li",
"Ping Luo"
] | 2311.14580 | 17,968 | https://openreview.net/forum?id=kZEXgtMNNo |
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[] | Spotlight Poster | [] | Joint embedding (JE) architectures have emerged as a promising avenue for ac-quiring transferable data representations. A key obstacle to using JE methods,however, is the inherent challenge of evaluating learned representations withoutaccess to a downstream task, and an annotated dataset. Without efficient and re-liable evaluation, it is difficult to iterate on architectural and training choices forJE methods. In this paper, we introduce LiDAR (Linear Discriminant AnalysisRank), a metric designed to measure the quality of representations within JE archi-tectures. Our metric addresses several shortcomings of recent approaches basedon feature covariance rank by discriminating between informative and uninforma-tive features. In essence, LiDAR quantifies the rank of the Linear DiscriminantAnalysis (LDA) matrix associated with the surrogate SSL task—a measure thatintuitively captures the information content as it pertains to solving the SSL task.We empirically demonstrate that LiDAR significantly surpasses naive rank basedapproaches in its predictive power of optimal hyperparameters. Our proposed cri-terion presents a more robust and intuitive means of assessing the quality of rep-resentations within JE architectures, which we hope facilitates broader adoptionof these powerful techniques in various domains. | [] | [] | LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures | [
"Vimal Thilak",
"Chen Huang",
"Omid Saremi",
"Laurent Dinh",
"Hanlin Goh",
"Preetum Nakkiran",
"Joshua M. Susskind",
"Etai Littwin"
] | 2312.04000 | 18,199 | https://openreview.net/forum?id=f3g5XpL9Kb |
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[] | Spotlight Poster | [] | Transfer learning with a small amount of target data is an effective and common approach to adapting a pre-trained model to distribution shifts. In some situations, target data labels may be expensive to obtain, so we may only have access to a limited number of target data points. To make the most of a very small target dataset, we propose a lightweight, sample-efficient approach that learns a diverse set of features and adapts to a target distribution by interpolating these features. Our approach, Project and Probe (Pro$^2$), first learns a linear projection that maps a pre-trained embedding onto orthogonal directions while being predictive of labels in the source dataset. The goal of this step is to learn a variety of predictive features, so that at least some of them remain useful after distribution shift. Pro$^2$ then learns a linear classifier on top of these projected features using a small target dataset. Theoretically, we find that Pro$^2$ results in more sample-efficient generalization by inducing a favorable bias-variance tradeoff. Our experiments on four datasets, with multiple distribution shift settings for each, show that Pro$^2$ improves performance by 5-15% when given limited target data compared to prior methods such as standard linear probing. | [] | [] | Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features | [
"Annie S Chen",
"Yoonho Lee",
"Amrith Setlur",
"Sergey Levine",
"Chelsea Finn"
] | 2302.05441 | 18,197 | https://openreview.net/forum?id=f6CBQYxXvr |
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[] | Poster | [] | Generating 3D ligand molecules that bind to specific protein targets via diffusion models has shown great promise for structure-based drug design. The key idea is to disrupt molecules into noise through a fixed forward process and learn its reverse process to generate molecules from noise in a denoising way. However, existing diffusion models primarily focus on incorporating protein-ligand interaction information solely in the reverse process, and neglect the interactions in the forward process. The inconsistency between forward and reverse processes may impair the binding affinity of generated molecules towards target protein. In this paper, we propose a novel Interaction Prior-guided Diffusion model (IPDiff) for the protein-specific 3D molecular generation by introducing geometric protein-ligand interactions into both diffusion and sampling process. Specifically, we begin by pretraining a protein-ligand interaction prior network (IPNet) by utilizing the binding affinity signals as supervision. Subsequently, we leverage the pretrained prior network to (1) integrate interactions between the target protein and the molecular ligand into the forward process for adapting the molecule diffusion trajectories (prior-shifting), and (2) enhance the binding-aware molecule sampling process (prior-conditioning). Empirical studies on CrossDocked2020 dataset show IPDiff can generate molecules with more realistic 3D structures and state-of-the-art binding affinities towards the protein targets, with up to -6.42 Avg. Vina Score, while maintaining proper molecular properties. | [] | [] | Protein-Ligand Interaction Prior for Binding-aware 3D Molecule Diffusion Models | [
"Zhilin Huang",
"Ling Yang",
"Xiangxin Zhou",
"Zhilong Zhang",
"Wentao Zhang",
"Xiawu Zheng",
"Jie Chen",
"Yu Wang",
"Bin CUI",
"Wenming Yang"
] | 17,740 | https://openreview.net/forum?id=qH9nrMNTIW |
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[] | Poster | [] | Federated learning (FL) is an emerging learning paradigm in which a set of distributed clients learns a task under the coordination of a central server. The FedAvg algorithm is one of the most widely used methods to solve FL problems. In FedAvg, the learning rate is a constant rather than changing adaptively. Adaptive gradient methods have demonstrated superior performance over the constant learning rate schedules in non-distributed settings, and they have recently been adapted to FL. However, the majority of these methods are designed for unconstrained settings. Meanwhile, many crucial FL applications, like disease diagnosis and biomarker identification, often rely on constrained formulations such as Lasso and group Lasso. It remains an open question as to whether adaptive gradient methods can be effectively applied to FL problems with constrains. In this work, we introduce \textbf{FedDA}, a novel adaptive gradient framework for FL. This framework utilizes a restarted dual averaging technique and is compatible with a range of gradient estimation methods and adaptive learning rate schedules. Specifically, an instantiation of our framework \textbf{FedDA-MVR} achieves gradient complexity $\tilde{O}(K^{-1}\epsilon^{-1.5})$ and communication complexity $\tilde{O}(K^{-0.25}\epsilon^{-1.25})$ for finding a stationary point $\epsilon$ in the constrained setting. We conduct experiments over both constrained and unconstrained tasks to confirm the effectiveness of our approach. | [] | [] | FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization | [
"Junyi Li",
"Feihu Huang",
"Heng Huang"
] | 17,966 | https://openreview.net/forum?id=kjn99xFUF3 |
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[] | Poster | [] | Distributed optimization (DO) approaches for saddle point problems (SPP) have recently gained in popularity due to the critical role they play in machine learning (ML). Existing works mostly target smooth unconstrained objectives in Euclidean space, whereas ML problems often involve constraints or non-smooth regularization, which results in a need for composite optimization. Moreover, although non-smooth regularization often serves to induce structure (e.g., sparsity), standard aggregation schemes in distributed optimization break this structure. Addressing these issues, we propose Federated Dual Extrapolation (FeDualEx), an extra-step primal-dual algorithm with local updates, which is the first of its kind to encompass both saddle point optimization and composite objectives under the distributed paradigm. Using a generalized notion of Bregman divergence, we analyze its convergence and communication complexity in the homogeneous setting. Furthermore, the empirical evaluation demonstrates the effectiveness of FeDualEx for inducing structure in these challenging settings. | [] | [] | Local Composite Saddle Point Optimization | [
"Site Bai",
"Brian Bullins"
] | 17,965 | https://openreview.net/forum?id=kklwv4c4dI |
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[] | Spotlight Poster | [] | Current privacy research on large language models (LLMs) primarily focuses on the issue of extracting memorized training data. At the same time, models’ inference capabilities have increased drastically. This raises the key question of whether current LLMs could violate individuals’ privacy by inferring personal attributes from text given at inference time. In this work, we present the first comprehensive study on the capabilities of pretrained LLMs to infer personal attributes from text. We construct a dataset consisting of real Reddit profiles, and show that current LLMs can infer a wide range of personal attributes (e.g., location, income, sex), achieving up to 85% top-1 and 95.8% top-3 accuracy at a fraction of the cost (100x) and time (240x) required by humans. As people increasingly interact with LLM-powered chatbots across all aspects of life, we also explore the emerging threat of privacy-invasive chatbots trying to extract personal information through seemingly benign questions. Finally, we show that common mitigations, i.e., text anonymization and model alignment, are currently ineffective at protecting user privacy against LLM inference. Our findings highlight that current LLMs can infer personal data at a previously unattainable scale. In the absence of working defenses, we advocate for a broader discussion around LLM privacy implications beyond memorization, striving for stronger and wider privacy protection. | [] | [] | Beyond Memorization: Violating Privacy via Inference with Large Language Models | [
"Robin Staab",
"Mark Vero",
"Mislav Balunovic",
"Martin Vechev"
] | 2310.07298 | 17,964 | https://openreview.net/forum?id=kmn0BhQk7p |
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[] | Poster | [] | Text-to-3D generation has made remarkable progress recently, particularly with methods based on Score Distillation Sampling (SDS) that leverages pre-trained 2D diffusion models. While the usage of classifier-free guidance is well acknowledged to be crucial for successful optimization, it is considered an auxiliary trick rather than the most essential component. In this paper, we re-evaluate the role of classifier-free guidance in score distillation and discover a surprising finding: the guidance alone is enough for effective text-to-3D generation tasks. We name this method Classifier Score Distillation (CSD), which can be interpreted as using an implicit classification model for generation. This new perspective reveals new insights for understanding existing techniques. We validate the effectiveness of CSD across a variety of text-to-3D tasks including shape generation, texture synthesis, and shape editing, achieving results superior to those of state-of-the-art methods. | [] | [] | Text-to-3D with Classifier Score Distillation | [
"Xin Yu",
"Yuan-Chen Guo",
"Yangguang Li",
"Ding Liang",
"Song-Hai Zhang",
"XIAOJUAN QI"
] | 2310.19415 | 17,961 | https://openreview.net/forum?id=ktG8Tun1Cy |
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[] | Poster | [] | Malicious server (MS) attacks have enabled the scaling of data stealing in federated learning to large batch sizes and secure aggregation, settings previously considered private. However, many concerns regarding the client-side detectability of MS attacks were raised, questioning their practicality. In this work, for the first time, we thoroughly study client-side detectability. We first demonstrate that all prior MS attacks are detectable by principled checks, and formulate a necessary set of requirements that a practical MS attack must satisfy. Next, we propose SEER, a novel attack framework that satisfies these requirements. The key insight of SEER is the use of a secret decoder, jointly trained with the shared model. We show that SEER can steal user data from gradients of realistic networks, even for large batch sizes of up to 512 and under secure aggregation. Our work is a promising step towards assessing the true vulnerability of federated learning in real-world settings. | [] | [] | Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning | [
"Kostadin Garov",
"Dimitar Iliev Dimitrov",
"Nikola Jovanović",
"Martin Vechev"
] | 2306.03013 | 17,962 | https://openreview.net/forum?id=krx55l2A6G |
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[] | Poster | [] | Connectionist Temporal Classification (CTC) is a widely used criterion for training supervised sequence-to-sequence (seq2seq) models. It learns the alignments between the input and output sequences, by marginalizing over the perfect alignments (that yield the ground truth), at the expense of the imperfect ones. This dichotomy, and in particular the equal treatment of all perfect alignments, results in a lack of controllability over the predicted alignments. This controllability is essential for capturing properties that hold significance in real-world applications.Here we propose Align With Purpose (AWP), a general Plug-and-Play framework for enhancing a desired property in models trained with the CTC criterion. We do that by complementing the CTC loss with an additional loss term that prioritizes alignments according to a desired property. AWP does not require any intervention in the CTC loss function, and allows to differentiate between both perfect and imperfect alignments for a variety of properties. We apply our framework in the domain of Automatic Speech Recognition (ASR) and show its generality in terms of property selection, architectural choice, and scale of training dataset (up to 280,000 hours). To demonstrate the effectiveness of our framework, we apply it to two unrelated properties: token emission time for latency optimization and word error rate (WER). For the former, we report an improvement of up to 590ms in latency optimization with a minor reduction in WER, and for the latter, we report a relative improvement of 4.5\% in WER over the baseline models. To the best of our knowledge, these applications have never been demonstrated to work on this scale of data. Notably, our method can be easily implemented using only a few lines of code and can be extended to other alignment-free loss functions and to domains other than ASR. | [] | [] | Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework | [
"Eliya Segev",
"Maya Alroy",
"Ronen Katsir",
"Noam Wies",
"Ayana Shenhav",
"Yael Sapir Ben-Oren",
"David Zar",
"Oren Tadmor",
"Jacob Bitterman",
"Amnon Shashua",
"Tal Rosenwein"
] | 2307.01715 | 18,186 | https://openreview.net/forum?id=fUGhVYPVRM |
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[] | Poster | [
"https://github.com/noa-cohen/MeaningfulDiversityInIR"
] | Image restoration problems are typically ill-posed in the sense that each degraded image can be restored in infinitely many valid ways. To accommodate this, many works generate a diverse set of outputs by attempting to randomly sample from the posterior distribution of natural images given the degraded input. Here we argue that this strategy is commonly of limited practical value because of the heavy tail of the posterior distribution. Consider for example inpainting a missing region of the sky in an image. Since there is a high probability that the missing region contains no object but clouds, any set of samples from the posterior would be entirely dominated by (practically identical) completions of sky. However, arguably, presenting users with only one clear sky completion, along with several alternative solutions such as airships, birds, and balloons, would better outline the set of possibilities. In this paper, we initiate the study of **meaningfully diverse** image restoration. We explore several post-processing approaches that can be combined with any diverse image restoration method to yield semantically meaningful diversity. Moreover, we propose a practical approach for allowing diffusion based image restoration methods to generate meaningfully diverse outputs, while incurring only negligent computational overhead. We conduct extensive user studies to analyze the proposed techniques, and find the strategy of reducing similarity between outputs to be significantly favorable over posterior sampling. | https://noa-cohen.github.io/MeaningfulDiversityInIR/ | [] | [] | From Posterior Sampling to Meaningful Diversity in Image Restoration | [
"Noa Cohen",
"Hila Manor",
"Yuval Bahat",
"Tomer Michaeli"
] | 2310.16047 | 18,179 | https://openreview.net/forum?id=ff2g30cZxj |
[] | Poster | [] | A popular approach to sample a diffusion-based generative model is to solve an ordinary differential equation (ODE). In existing samplers, the coefficients of the ODE solvers are pre-determined by the ODE formulation, the reverse discrete timesteps, and the employed ODE methods. In this paper, we consider accelerating several popular ODE-based sampling processes (including EDM, DDIM, and DPM-Solver) by optimizing certain coefficients via improved integration approximation (IIA). We propose to minimize, for each time step, a mean squared error (MSE) function with respect to the selected coefficients. The MSE is constructed by applying the original ODE solver for a set of fine-grained timesteps, which in principle provides a more accurate integration approximation in predicting the next diffusion state. The proposed IIA technique does not require any change of a pre-trained model, and only introduces a very small computational overhead for solving a number of quadratic optimization problems. Extensive experiments show that considerably better FID scores can be achieved by using IIA-EDM, IIA-DDIM, and IIA-DPM-Solver than the original counterparts when the neural function evaluation (NFE) is small (i.e., less than 25). | [] | [] | On Accelerating Diffusion-Based Sampling Processes via Improved Integration Approximation | [
"Guoqiang Zhang",
"Kenta Niwa",
"W. Bastiaan Kleijn"
] | 2304.11328 | 17,960 | https://openreview.net/forum?id=ktJAF3lxbi |
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[] | Poster | [] | AI systems based on deep learning have reached or surpassed human performance in a range of narrow domains. In coming decades, artificial general intelligence (AGI) may surpass human capabilities at many critical tasks. In this position paper, we examine the technical difficulty of fine-tuning hypothetical AGI systems based on pretrained deep models to pursue goals that are aligned with human interests. We argue that, if trained like today's most capable models, AGI systems could learn to act deceptively to receive higher reward, learn internally-represented goals which generalize beyond their fine-tuning distributions, and pursue those goals using power-seeking strategies. We review emerging evidence for these properties. AGIs with these properties would be difficult to align and may appear aligned even when they are not. | [] | [] | The Alignment Problem from a Deep Learning Perspective | [
"Richard Ngo",
"Lawrence Chan",
"Sören Mindermann"
] | 2209.00626 | 18,177 | https://openreview.net/forum?id=fh8EYKFKns |
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[] | Spotlight Poster | [] | The success of AI assistants based on language models (LLMs) hinges crucially on Reinforcement Learning from Human Feedback (RLHF), which enables the generation of responses more aligned with human preferences. As universal AI assistants, there's a growing expectation for them to perform consistently across various domains. However, previous work shows that Reinforcement Learning (RL) often exploits shortcuts to attain high rewards and overlooks challenging samples.This focus on quick reward gains undermines both the stability in training and the model's ability to generalize to new, unseen data.In this work, we propose a novel approach that can learn a consistent policy via RL across various data groups or domains. Given the challenges associated with acquiring group annotations, our method automatically classifies data into different groups, deliberately maximizing performance variance.Then, we optimize the policy to perform well on challenging groups. Lastly, leveraging the established groups, our approach adaptively adjusts the exploration space, allocating more learning capacity to more challenging data and preventing the model from over-optimizing on simpler data. Experimental results indicate that our approach significantly enhances training stability and model generalization. | [] | [] | Improving Generalization of Alignment with Human Preferences through Group Invariant Learning | [
"Rui Zheng",
"Wei Shen",
"Yuan Hua",
"Wenbin Lai",
"Shihan Dou",
"Yuhao Zhou",
"Zhiheng Xi",
"Xiao Wang",
"Haoran Huang",
"Tao Gui",
"Qi Zhang",
"Xuanjing Huang"
] | 2310.11971 | 18,165 | https://openreview.net/forum?id=fwCoLe3TAX |
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[] | Poster | [] | Signal restoration is an important constrained optimization problem with significant applications in various domains. Although non-convex constrained optimization problems have been shown to perform better than convex counterparts in terms of reconstruction quality, convex constrained optimization problems have been preferably for its global optima guarantees. Despite the success of non-convex methods in a large number of applications, it is not an overstatement to say that there is little or no hope for non-convex problems to ensure global optima. In this paper, for the first time, we develop invex constrained optimization theory to mitigate the loss of guarantees for global optima in non-convex constrained inverse problems, where the invex function is a mapping where any critical point is a global minimizer. We also develop relevant theories to extend the global optima guarantee to a set of quasi-invex functions - the largest optimizable mappings. More specifically, we propose a family of invex/quasi-invex of functions for handling constrained inverse problems using the non-convex setting along with guarantees for their global optima. Our experimental evaluation shows that the proposed approach is very promising and can aid in extending existing convex optimization algorithms, such as the alternating direction method of multipliers, and accelerated proximal gradient methods. | [] | [] | Global Optimality for Non-linear Constrained Restoration Problems via Invexity | [
"Samuel Pinilla",
"Jeyan Thiyagalingam"
] | 18,163 | https://openreview.net/forum?id=fyTPWfXtcc |
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[] | Poster | [] | Image codecs are typically optimized to trade-off bitrate vs. distortion metrics. At low bitrates, this leads to compression artefacts which are easily perceptible, even when training with perceptual distortion metrics or adversarial losses. To improve image quality, and to make it less dependent on the bitrate, we propose to leverage diffusion models as a decoder module, which rely on an iterative decoding process instead of feed-forward decoders trained using MSE or LPIPS distortions used in most neural codecs. In addition to conditioning the model on a vector-quantized image representation, we also condition on a global textual image description to provide additional context. We dub our model PerCo for ''perceptual compression'', and compare it to state-of-the-art codecs at bitrates from 0.1 down to 0.003 bits per pixel (bpp). The latter rate is an order of magnitude smaller than those considered in most prior work. At this bitrate a 512x768 Kodak image is encoded in just 148 bytes. Despite this ultra-low bitrate, our approach maintains the ability to reconstruct realistic images. We find that our model leads to reconstructions with state-of-the-art visual quality as measured by FID and KID, and that the visual quality is less dependent on the bitrate than previous methods. Image codecs are typically optimized to trade-off bitrate vs. distortion metrics. At low bitrates, this leads to compression artefacts which are easily perceptible, even when training with perceptual or adversarial losses. To improve image quality, and to make it less dependent on the bitrate, we propose to decode with iterative diffusion models, instead of feed-forward decoders trained using MSE or LPIPS distortions used in most neural codecs. In addition to conditioning the model on a vector-quantized image representation, we also condition on a global textual image description to provide additional context. We dub our model PerCo for ''perceptual compression'', and compare it to state-of-the-art codecs at rates from 0.1 down to 0.003 bits per pixel. The latter rate is an order of magnitude smaller than those considered in most prior work. At this bitrate a 512x768 Kodak image is encoded in less than 153 bytes. Despite this ultra-low bitrate, our approach maintains the ability to reconstruct realistic images. We find that our model leads to reconstructions with state-of-the-art visual quality as measured by FID and KID, and that the visual quality is less dependent on the bitrate than previous methods. | [] | [] | Towards image compression with perfect realism at ultra-low bitrates | [
"Marlene Careil",
"Matthew J. Muckley",
"Jakob Verbeek",
"Stéphane Lathuilière"
] | 2310.10325 | 17,959 | https://openreview.net/forum?id=ktdETU9JBg |
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[] | Poster | [] | Sampling-based algorithms, which eliminate "unimportant" computations during forward and/or backpropagation (BP), offer potential solutions to accelerate neural network training. However, since sampling introduces approximations to training, such algorithms may not consistently maintain accuracy across various tasks. In this work, we introduce a variance-controlled adaptive sampling (VCAS) method designed to minimize the computational load of BP. VCAS computes an unbiased stochastic gradient with fine-grained layerwise importance sampling in data dimension for activation gradient calculation and leverage score sampling in token dimension for weight gradient calculation. To preserve accuracy, we control the additional variance introduced by learning the sample ratio jointly with model parameters during training. We assessed VCAS on multiple fine-tuning and pre-training tasks in both vision and natural language domains. On all the tasks, VCAS can preserve the original training loss trajectory and validation accuracy with an up to 73.87% FLOPs reduction of BP and 49.58% FLOPs reduction of the whole training process. | [] | [] | Efficient Backpropagation with Variance Controlled Adaptive Sampling | [
"Ziteng Wang",
"Jianfei Chen",
"Jun Zhu"
] | 2402.17227 | 18,152 | https://openreview.net/forum?id=gEwKAZZmSw |
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[] | Poster | [] | Reliably reconstructing physical fields from sparse sensor data is a challenge that frequenty arises in many scientific domains. In practice, the process generating the data is often not known to sufficient accuracy. Therefore, there is a growing interest in the deep neural network route to the problem. In this work, we present a novel approach that learns a continuous representation of the field using implicit neural representations (INR). Specifically, after factorizing spatiotemporal variability into spatial and temporal components using the technique of separation of variables, the method learns relevant basis functions from sparsely sampled irregular data points to thus develop a continuous representation of the data. In experimental evaluations, the proposed model outperforms recent INR methods, offering superior reconstruction quality on simulation data from a state of the art climate model and on a second dataset that comprises of ultra-high resolution satellite-based sea surface temperature field. | [] | [] | Continuous Field Reconstruction from Sparse Observations with Implicit Neural Networks | [
"Xihaier Luo",
"Wei Xu",
"Balu Nadiga",
"Yihui Ren",
"Shinjae Yoo"
] | 2401.11611 | 17,958 | https://openreview.net/forum?id=kuTZMZdCPZ |
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[] | Poster | [] | Training large language models to follow instructions makes them perform better on a wide range of tasks and generally become more helpful. However, a perfectly helpful model will follow even the most malicious instructions and readily generate harmful content.In this paper, we raise concerns over the safety of models that only emphasize helpfulness, not harmlessness, in their instruction-tuning.We show that several popular instruction-tuned models are highly unsafe. Moreover, we show that adding just 3\% safety examples (a few hundred demonstrations) when fine-tuning a model like LLaMA can substantially improve its safety. Our safety-tuning does not make models significantly less capable or helpful as measured by standard benchmarks. However, we do find exaggerated safety behaviours, where too much safety-tuning makes models refuse perfectly safe prompts if they superficially resemble unsafe ones. As a whole, our results illustrate trade-offs in training LLMs to be helpful and training them to be safe. | [] | [] | Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions | [
"Federico Bianchi",
"Mirac Suzgun",
"Giuseppe Attanasio",
"Paul Rottger",
"Dan Jurafsky",
"Tatsunori Hashimoto",
"James Zou"
] | 2309.07875 | 18,143 | https://openreview.net/forum?id=gT5hALch9z |
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[] | Poster | [] | We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e.g., depth maps). JointNet is extended from a pre-trained text-to-image diffusion model, where a copy of the original network is created for the new dense modality branch and is densely connected with the RGB branch. The RGB branch is locked during network fine-tuning, which enables efficient learning of the new modality distribution while maintaining the strong generalization ability of the large-scale pre-trained diffusion model.We demonstrate the effectiveness of JointNet by using the RGB-D diffusion as an example and through extensive experiments, showcasing its applicability in a variety of applications, including joint RGB-D generation, dense depth prediction, depth-conditioned image generation, and high-resolution 3D panorama generation. | [] | [] | JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling | [
"Jingyang Zhang",
"Shiwei Li",
"Yuanxun Lu",
"Tian Fang",
"David Neil McKinnon",
"Yanghai Tsin",
"Long Quan",
"Yao Yao"
] | 2310.06347 | 17,957 | https://openreview.net/forum?id=kv5xE1p3jz |
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[] | Poster | [] | Measuring geometric similarity between high-dimensional network representations is a topic of longstanding interest to neuroscience and deep learning. Although many methods have been proposed, only a few works have rigorously analyzed their statistical efficiency or quantified estimator uncertainty in data-limited regimes. Here, we derive upper and lower bounds on the worst-case convergenceof standard estimators of shape distance—a measure of representational dissimilarity proposed by Williams et al. (2021). These bounds reveal the challenging nature of the problem in high-dimensional feature spaces. To overcome these challenges, we introduce a novel method-of-moments estimator with a tunable bias-variance tradeoff parameterized by an upper bound on bias. We show that this estimator achieves superior performance to standard estimators in simulation and on neural data, particularly in high-dimensional settings. Our theoretical work and estimator thus respectively define and dramatically expand the scope of neural data for which geometric similarity can be accurately measured. | [] | [] | Estimating Shape Distances on Neural Representations with Limited Samples | [
"Dean A Pospisil",
"Brett W. Larsen",
"Sarah E Harvey",
"Alex H Williams"
] | 2310.05742 | 17,956 | https://openreview.net/forum?id=kvByNnMERu |
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[] | Poster | [] | In this work we present successor heads: attention heads that increment tokens with a natural ordering, such as numbers, months, and days. For example, successor heads increment ‘Monday’ into ‘Tuesday’. We explain the successor head behavior with an approach rooted in mechanistic interpretability, the field that aims to explain how models complete tasks in human-understandable terms. Existing research in this area has found interpretable language model components in small toy models. However, results in toy models have not yet led to insights that explain the internals of frontier models and little is currently understood about the internal operations of large language models. In this paper, we analyze the behavior of successor heads in large language models (LLMs) and find that they implement abstract representations that are common to different architectures. They form in LLMs with as few as 31 million parameters, and at least as many as 12 billion parameters, such as GPT-2, Pythia, and Llama-2. We find a set of ‘mod 10’ features that underlie how successor heads increment in LLMs across different architectures and sizes. We perform vector arithmetic with these features to edit head behavior and provide insights into numeric representations within LLMs. Additionally, we study the behavior of successor heads on natural language data,identifying interpretable polysemanticity in a Pythia successor head. | [] | [] | Successor Heads: Recurring, Interpretable Attention Heads In The Wild | [
"Rhys Gould",
"Euan Ong",
"George Ogden",
"Arthur Conmy"
] | 2312.09230 | 17,955 | https://openreview.net/forum?id=kvcbV8KQsi |
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[] | Poster | [] | Contrastive learning, while highly effective for a lot of tasks, shows limited improvement in ordinal regression. We find that the limitation comes from the predefined strong data augmentations employed in contrastive learning. Intuitively, for ordinal regression datasets, the discriminative information (ordinal content information) contained in instances is subtle. The strong augmentations can easily overshadow or diminish this ordinal content information. As a result, when contrastive learning is used to extract common features between weakly and strongly augmented images, the derived features often lack this essential ordinal content, rendering them less useful in training models for ordinal regression. To improve contrastive learning's utility for ordinal regression, we propose a novel augmentation method to replace the predefined strong argumentation based on the principle of "minimal change". Our method is designed in a generative manner that can effectively generate images with different styles but contains desired ordinal content information. Extensive experiments validate the effectiveness of our proposed method, which serves as a plug-and-play solution and consistently improves the performance of existing state-of-the-art methods in ordinal regression tasks. | [] | [] | Enhancing Contrastive Learning for Ordinal Regression via Ordinal Content Preserved Data Augmentation | [
"Jiyang Zheng",
"Yu Yao",
"Bo Han",
"Dadong Wang",
"Tongliang Liu"
] | 17,954 | https://openreview.net/forum?id=kx2XZlmgB1 |
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[] | Spotlight Poster | [] | Existing efforts on quantifying privacy implications for large language models (LLMs) solely focus on measuring leakage of training data. In this work, we shed light on the often-overlooked interactive settings where an LLM receives information from multiple sources and generates an output to be shared with other entities, creating the potential of exposing sensitive input data in inappropriate contexts. In these scenarios, humans nat- urally uphold privacy by choosing whether or not to disclose information depending on the context. We ask the question “Can LLMs demonstrate an equivalent discernment and reasoning capability when considering privacy in context?” We propose CONFAIDE, a benchmark grounded in the theory of contextual integrity and designed to identify critical weaknesses in the privacy reasoning capabilities of instruction-tuned LLMs. CONFAIDE consists of four tiers, gradually increasing in complexity, with the final tier evaluating contextual privacy reasoning and theory of mind capabilities. Our experiments show that even commercial models such as GPT-4 and ChatGPT reveal private information in contexts that humans would not, 39% and 57% of the time, respectively, highlighting the urgent need for a new direction of privacy-preserving approaches as we demonstrate a larger underlying problem stemmed in the models’ lack of reasoning capabilities. | [] | [] | Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory | [
"Niloofar Mireshghallah",
"Hyunwoo Kim",
"Xuhui Zhou",
"Yulia Tsvetkov",
"Maarten Sap",
"Reza Shokri",
"Yejin Choi"
] | 2310.17884 | 18,131 | https://openreview.net/forum?id=gmg7t8b4s0 |
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[] | Poster | [] | Text generation under constraints have seen increasing interests in natural language processing, especially with the rapidly improving capabilities of large language models. However, existing benchmarks for constrained generation usually focus on fixed constraint types (e.g. generate a sentence containing certain words) that have proved to be easy for state-of-the-art models like GPT-4. We present COLLIE, a grammar-based framework that allows the specification of rich, compositional constraints with diverse generation levels (word, sentence, paragraph, passage) and modeling challenges (e.g. language understanding, logical reasoning, counting, semantic planning). We also develop tools for automatic extraction of task instances given a constraint structure and a raw text corpus. Using COLLIE, we compile the COLLIE-v1 dataset with 1,132 instances comprising 13 constraint structures. We perform systematic experiments across five state-of-the-art instruction-tuned language models and analyze their performances to reveal shortcomings. COLLIE is designed to be extensible and lightweight, and we hope the community finds it useful to develop more complex constraints and evaluations in the future. | [] | [] | COLLIE: Systematic Construction of Constrained Text Generation Tasks | [
"Shunyu Yao",
"Howard Chen",
"Austin W. Hanjie",
"Runzhe Yang",
"Karthik R Narasimhan"
] | 2307.08689 | 17,952 | https://openreview.net/forum?id=kxgSlyirUZ |
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[] | Poster | [] | Deep generative diffusion models are a promising avenue for 3D $\textit{de novo}$ molecular design in material science and drug discovery. However, their utility is still constrained by suboptimal performance with large molecular structures and limited training data. Addressing this gap, we explore the design space of E(3) equivariant diffusion models, focusing on previously blank spots. Our extensive comparative analysis evaluates the interplay between continuous and discrete state spaces. Out of this investigation, we introduce the EQGAT-diff model, which consistently surpasses the performance of established models on the QM9 and GEOM-Drugs datasets by a large margin.Distinctively, EQGAT-diff takes continuous atomic positions while chemical elements and bond types are categorical and employ a time-dependent loss weighting that significantly increases training convergence and the quality of generated samples.To further strengthen the applicability of diffusion models to limited training data, we examine the transferability of EQGAT-diff trained on the large PubChem3D dataset with implicit hydrogens to target distributions with explicit hydrogens. Fine-tuning EQGAT-diff for a couple of iterations further pushes state-of-the-art performance across datasets.We envision that our findings will find applications in structure-based drug design, where the accuracy of generative models for small datasets of complex molecules is critical. | [] | [] | Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation | [
"Tuan Le",
"Julian Cremer",
"Frank Noe",
"Djork-Arné Clevert",
"Kristof T Schütt"
] | 2309.17296 | 17,951 | https://openreview.net/forum?id=kzGuiRXZrQ |
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[] | Poster | [] | Accurate 3D lane estimation is pivotal for autonomous driving safety. However, prevalent monocular techniques suffer from depth loss and lighting variations, hampering accurate 3D lane detection. Conversely, LiDAR points offer geometric cues and enable precise localization. In this paper, we present DV-3DLane, a novel end-to-end Dual-View multi-modal 3D Lane detection framework that synergizes the strengths of both images and LiDAR points. Technically, we propose to learn multi-modal features in dual-view spaces, i.e., perspective view (PV) and bird's-eye-view (BEV), effectively tapping the modal-specific information. To achieve this, we propose three designs: 1) We design a bidirectional feature fusion strategy that integrates multi-modal features into each view space, exploiting the unique strengths of each space. 2) We propose a unified query generation approach that offers queries lane-aware prior knowledge from both views. 3) We introduce a 3D dual-view deformable attention mechanism, aggregating discriminative features from both PV and BEV into queries for accurate 3D lane detection. Extensive experiments on the public benchmark, OpenLane, demonstrate the efficacy and efficiency of DV-3DLane, i.e., it achieves state-of-the-art performance, with a remarkable 9.5 gain in F1 score and a substantial 49.8% reduction in errors. | [] | [] | DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation | [
"Yueru Luo",
"Shuguang Cui",
"Zhen Li"
] | 17,950 | https://openreview.net/forum?id=l1U6sEgYkb |
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[] | Poster | [] | Zero Redundancy Optimizer (ZeRO) has been used to train a wide range of large language models on massive GPU clusters due to its ease of use, efficiency, and good scalability. However, when training on low-bandwidth clusters, and/or when small batch size per GPU is used, ZeRO’s effective throughput is limited due to communication overheads. To alleviate this limitation, this paper introduces ZeRO++ composing of three communication volume reduction techniques (lowprecision all-gather, data remapping, and low-precision gradient averaging) to significantly reduce the communication volume up to 4x that enables up to 2.16x better throughput at 384 GPU scale. Our results also show ZeRO++ can speedup the RLHF by 3.3x compared to vanilla ZeRO. To verify the convergence of ZeRO++, we test up to 13B model for pretraining with 8/6-bits all gather and up to 30B model for finetuning with 4/2-bits all gather, and demonstrate on-par accuracy as original ZeRO (aka standard training). As a byproduct, the model trained with ZeRO++ is naturally weight-quantized, which can be directly used for inference without post-training quantization or quantization-aware training. | [] | [] | ZeRO++: Extremely Efficient Collective Communication for Large Model Training | [
"Guanhua Wang",
"Heyang Qin",
"Sam Ade Jacobs",
"Xiaoxia Wu",
"Connor Holmes",
"Zhewei Yao",
"Samyam Rajbhandari",
"Olatunji Ruwase",
"Feng Yan",
"Lei Yang",
"Yuxiong He"
] | 18,124 | https://openreview.net/forum?id=gx2BT0a9MQ |
||
[] | Spotlight Poster | [] | Large-scale graph machine learning is challenging as the complexity of learning models scales with the graph size. Subsampling the graph is a viable alternative, but sampling on graphs is nontrivial as graphs are non-Euclidean. Existing graph sampling techniques require not only computing the spectra of large matrices but also repeating these computations when the graph changes, e.g., grows. In this paper, we introduce a signal sampling theory for a type of graph limit---the graphon. We prove a Poincaré inequality for graphon signals and show that complements of node subsets satisfying this inequality are unique sampling sets for Paley-Wiener spaces of graphon signals. Exploiting connections with spectral clustering and Gaussian elimination, we prove that such sampling sets are consistent in the sense that unique sampling sets on a convergent graph sequence converge to unique sampling sets on the graphon. We then propose a related graphon signal sampling algorithm for large graphs, and demonstrate its good empirical performance on graph machine learning tasks. | [] | [] | A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs | [
"Thien Le",
"Luana Ruiz",
"Stefanie Jegelka"
] | 17,949 | https://openreview.net/forum?id=l3qtSNsPvC |
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[] | Poster | [] | The scientific scale-up of large language models (LLMs) necessitates a comprehensive understanding of their scaling properties. However, the existing literature on the scaling properties only yields an incomplete answer: optimization loss decreases predictably as the model size increases, in line with established scaling law; yet no scaling law for task has been established and the task performances are far from predictable during scaling. Task performances typically show minor gains on small models until they improve dramatically once models exceed a size threshold, exemplifying the \`\`emergent abilities''. In this study, we discover that small models, although they exhibit minor performance, demonstrate critical and consistent task performance improvements that are not captured by conventional evaluation strategies due to insufficient measurement resolution. To measure such improvements, we introduce \textsc{PassUntil}, an evaluation strategy with theoretically infinite resolution, through massive sampling in the decoding phase. With \textsc{PassUntil}, we conduct a quantitative investigation into the scaling law of task performance. The investigation contains two parts. Firstly, a strict \textsl{task scaling law} that is not conventionally known to exist, is identified, enhancing the predictability of task performances. Remarkably, we are able to predict the performance of the 2.4B model on code generation with merely 0.05\% deviation before training starts, which is the first systematic attempt to verify predictable scaling proposed by GPT-4's report. Secondly, underpinned by \textsc{PassUntil}, we observe concrete evidence of emergent abilities and ascertain that they are not in conflict with the continuity of performance improvement. Their semblance to break-through is that their scaling curve cannot be fitted by standard scaling law function. We then introduce a mathematical definition for the emergent abilities. Through the definition, we refute a prevalent ``multi-step reasoning hypothesis'' regarding the genesis of emergent abilities and propose a new hypothesis with a satisfying fit to the observed scaling curve. | [] | [] | Predicting Emergent Abilities with Infinite Resolution Evaluation | [
"Shengding Hu",
"Xin Liu",
"Xu Han",
"Xinrong Zhang",
"Chaoqun He",
"Weilin Zhao",
"Yankai Lin",
"Ning Ding",
"Zebin Ou",
"Guoyang Zeng",
"Zhiyuan Liu",
"Maosong Sun"
] | 2310.03262 | 17,945 | https://openreview.net/forum?id=lDbjooxLkD |
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[] | Poster | [] | Learning multi-scale representations is central to semantic segmentation. We visualize the effective receptive field (ERF) of canonical multi-scale representations and point out two risks in learning them: \textit{scale inadequacy} and \textit{field inactivation}. To address these issues, a novel multi-scale learner, \textbf{varying window attention} (VWA), is presented. VWA leverages the local window attention (LWA) and disentangles LWA into the query window and context window, allowing the scale of context to vary for the query to learn representations at specific scales. However, varying the context to large-scale windows (enlarging ratio $R$) can significantly increase the memory footprint and computation cost ($R^2$ times larger than LWA). We propose a simple but professional re-scaling strategy to zero the extra induced cost without compromising any performance. In consequence, VWA shows great superiority to previous multi-scale learners. Furthermore, building upon VWA and employing various MLPs, we introduce a multi-scale decoder (MSD), \textbf{VWFormer}, to improve learning multi-scale representations in semantic segmentation. VWFormer achieves efficiency competitive with the most compute-friendly MSDs, like FPN and MLP decoder, but performs much better than any MSDs. For instance, at little extra overhead, $\sim 10$G FLOPs, VWFormer improves Mask2Former by $1.0\%-1.3\%$ mIoU. Using only half of the computation, VWFormer outperforms the popular UperNet by $1.0\%-2.1\%$ mIoU. | [] | [] | Multi-Scale Representations by Varying Window Attention for Semantic Segmentation | [
"Haotian Yan",
"Ming Wu",
"Chuang Zhang"
] | 2404.16573 | 17,946 | https://openreview.net/forum?id=lAhWGOkpSR |
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[] | Poster | [] | Incorporating expert demonstrations has empirically helped to improve the sample efficiency of reinforcement learning (RL). This paper quantifies theoretically to what extent this extra information reduces RL's sample complexity. Precisely, we study the demonstration-regularized reinforcement learning framework that leverages the expert demonstrations by $\mathrm{KL}$-regularization for a policy learned by behavior cloning. Our findings reveal that utilizing $N^{\mathrm{E}}$ expert demonstrations enables the identification of an optimal policy at a sample complexity of order $\widetilde{\mathcal{O}}(\mathrm{Poly}(S,A,H)/(\varepsilon^2 N^{\mathrm{E}}))$ in finite and $\widetilde{\mathcal{O}}(\mathrm{Poly}(d,H)/(\varepsilon^2 N^{\mathrm{E}}))$ in linear Markov decision processes, where $\varepsilon$is the target precision, $H$ the horizon, $A$ the number of action, $S$ the number of states in the finite case and $d$ the dimension of the feature space in the linear case. As a by-product, we provide tight convergence guarantees for the behaviour cloning procedure under general assumptions on the policy classes. Additionally, we establish that demonstration-regularized methods are provably efficient for reinforcement learning from human feedback (RLHF). In this respect, we provide theoretical evidence showing the benefits of KL-regularization for RLHF in tabular and linear MDPs. Interestingly, we avoid pessimism injection by employing computationally feasible regularization to handle reward estimation uncertainty, thus setting our approach apart from the prior works. | [] | [] | Demonstration-Regularized RL | [
"Daniil Tiapkin",
"Denis Belomestny",
"Daniele Calandriello",
"Eric Moulines",
"Alexey Naumov",
"Pierre Perrault",
"Michal Valko",
"Pierre Menard"
] | 2310.17303 | 17,944 | https://openreview.net/forum?id=lF2aip4Scn |
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[] | Spotlight Poster | [] | Convolution-BatchNorm (ConvBN) blocks are integral components in various computer vision tasks and other domains. A ConvBN block can operate in three modes: Train, Eval, and Deploy. While the Train mode is indispensable for training models from scratch, the Eval mode is suitable for transfer learning and beyond, and the Deploy mode is designed for the deployment of models. This paper focuses on the trade-off between stability and efficiency in ConvBN blocks: Deploy mode is efficient but suffers from training instability; Eval mode is widely used in transfer learning but lacks efficiency. To solve the dilemma, we theoretically reveal the reason behind the diminished training stability observed in the Deploy mode. Subsequently, we propose a novel Tune mode to bridge the gap between Eval mode and Deploy mode. The proposed Tune mode is as stable as Eval mode for transfer learning, and its computational efficiency closely matches that of the Deploy mode. Through extensive experiments in object detection, classification, and adversarial example generation across $5$ datasets and $12$ model architectures, we demonstrate that the proposed Tune mode retains the performance while significantly reducing GPU memory footprint and training time, thereby contributing efficient ConvBN blocks for transfer learning and beyond. Our method has been integrated into both PyTorch (general machine learning framework) and MMCV/MMEngine (computer vision framework). Practitioners just need one line of code to enjoy our efficient ConvBN blocks thanks to PyTorch's builtin machine learning compilers. | [] | [] | Efficient ConvBN Blocks for Transfer Learning and Beyond | [
"Kaichao You",
"Guo Qin",
"Anchang Bao",
"Meng Cao",
"Ping Huang",
"Jiulong Shan",
"Mingsheng Long"
] | 2305.11624 | 17,941 | https://openreview.net/forum?id=lHZm9vNm5H |
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[] | Poster | [] | Binary Neural networks (BNN) have emerged as an attractive computing paradigm for a wide range of low-power vision tasks. However, state-of-the-art (SOTA) BNNs do not yield any sparsity, and induce a significant number of non-binary operations. On the other hand, activation sparsity can be provided by spiking neural networks (SNN), that too have gained significant traction in recent times. Thanks to this sparsity, SNNs when implemented on neuromorphic hardware, have the potential to be significantly more power-efficient compared to traditional artifical neural networks (ANN). However, SNNs incur multiple time steps to achieve close to SOTA accuracy. Ironically, this increases latency and energy---costs that SNNs were proposed to reduce---and presents itself as a major hurdle in realizing SNNs’ theoretical gains in practice. This raises an intriguing question: *Can we obtain SNN-like sparsity and BNN-like accuracy and enjoy the energy-efficiency benefits of both?* To answer this question, in this paper, we present a training framework for sparse binary activation neural networks (BANN) using a novel variant of the Hoyer regularizer. We estimate the threshold of each BANN layer as the Hoyer extremum of a clipped version of its activation map, where the clipping value is trained using gradient descent with our Hoyer regularizer. This approach shifts the activation values away from the threshold, thereby mitigating the effect of noise that can otherwise degrade the BANN accuracy. Our approach outperforms existing BNNs, SNNs, and adder neural networks (that also avoid energy-expensive multiplication operations similar to BNNs and SNNs) in terms of the accuracy-FLOPs trade-off for complex image recognition tasks. Downstream experiments on object detection further demonstrate the efficacy of our approach. Lastly, we demonstrate the portability of our approach to SNNs with multiple time steps. Codes are publicly available [here](https://github.com/godatta/Ultra-Low-Latency-SNN). | [] | [] | Can we get the best of both Binary Neural Networks and Spiking Neural Networks for Efficient Computer Vision? | [
"Gourav Datta",
"Zeyu Liu",
"Peter Anthony Beerel"
] | 17,942 | https://openreview.net/forum?id=lGUyAuuTYZ |
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[] | Poster | [] | Hypergraph Neural Networks (HGNNs) have recently attracted much attention and exhibited satisfactory performance due to their superiority in high-order correlation modeling. However, it is noticed that the high-order modeling capability of hypergraph also brings increased computation complexity, which hinders its practical industrial deployment.In practice, we find that one key barrier to the efficient deployment of HGNNs is the high-order structural dependencies during inference.In this paper, we propose to bridge the gap between the HGNNs and inference-efficient Multi-Layer Perceptron (MLPs) to eliminate the hypergraph dependency of HGNNs and thus reduce computational complexity as well as improve inference speed. Specifically, we introduce LightHGNN and LightHGNN$^+$ for fast inference with low complexity. LightHGNN directly distills the knowledge from teacher HGNNs to student MLPs via soft labels, and LightHGNN$^+$ further explicitly injects reliable high-order correlations into the student MLPs to achieve topology-aware distillation and resistance to over-smoothing.Experiments on eight hypergraph datasets demonstrate that even without hypergraph dependency, the proposed LightHGNNs can still achieve competitive or even better performance than HGNNs and outperform vanilla MLPs by $16.3$ on average. Extensive experiments on three graph datasets further show the average best performance of our LightHGNNs compared with all other methods.Experiments on synthetic hypergraphs with 5.5w vertices indicate LightHGNNs can run $100\times$ faster than HGNNs, showcasing their ability for latency-sensitive deployments. | [] | [] | LightHGNN: Distilling Hypergraph Neural Networks into MLPs for 100x Faster Inference | [
"Yifan Feng",
"Yihe Luo",
"Shihui Ying",
"Yue Gao"
] | 2402.04296 | 17,940 | https://openreview.net/forum?id=lHasEfGsXL |
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[] | Poster | [] | Large Language Models like ChatGPT demonstrate a remarkable capacity to learn new concepts during inference without any fine-tuning. However, visual models trained to detect new objects during inference have been unable to replicate this ability, and instead either perform poorly or require meta-training and/or fine-tuning on similar objects. In this work, we propose a meta-learning algorithm that emulates Large Language Models by learning new visual concepts during inference without fine-tuning. Our approach leverages a frozen pre-trained feature extractor, and analogous to in-context learning, recasts meta-learning as sequence modeling over datapoints with known labels and a test datapoint with an unknown label. On 8 out of 11 meta-learning benchmarks, our approach---without meta-training or fine-tuning---exceeds or matches the state-of-the-art algorithm, P>M>F, which is meta-trained on these benchmarks. | [] | [] | Context-Aware Meta-Learning | [
"Christopher Fifty",
"Dennis Duan",
"Ronald Guenther Junkins",
"Ehsan Amid",
"Jure Leskovec",
"Christopher Re",
"Sebastian Thrun"
] | 2310.10971 | 17,939 | https://openreview.net/forum?id=lJYAkDVnRU |
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[] | Poster | [] | Local motion blur commonly occurs in real-world photography due to the mixing between moving objects and stationary backgrounds during exposure. Existing image deblurring methods predominantly focus on global deblurring, inadvertently affecting the sharpness of backgrounds in locally blurred images and wasting unnecessary computation on sharp pixels, especially for high-resolution images.This paper aims to adaptively and efficiently restore high-resolution locally blurred images. We propose a local motion deblurring vision Transformer (LMD-ViT) built on adaptive window pruning Transformer blocks (AdaWPT). To focus deblurring on local regions and reduce computation, AdaWPT prunes unnecessary windows, only allowing the active windows to be involved in the deblurring processes. The pruning operation relies on the blurriness confidence predicted by a confidence predictor that is trained end-to-end using a reconstruction loss with Gumbel-Softmax re-parameterization and a pruning loss guided by annotated blur masks. Our method removes local motion blur effectively without distorting sharp regions, demonstrated by its exceptional perceptual and quantitative improvements (+0.28dB) compared to state-of-the-art methods. In addition, our approach substantially reduces FLOPs by 66% and achieves more than a twofold increase in inference speed compared to Transformer-based deblurring methods. We will make our code and annotated blur masks publicly available. | [] | [] | Adaptive Window Pruning for Efficient Local Motion Deblurring | [
"Haoying Li",
"Jixin Zhao",
"Shangchen Zhou",
"Huajun Feng",
"Chongyi Li",
"Chen Change Loy"
] | 2306.14268 | 18,103 | https://openreview.net/forum?id=hI18CDyadM |
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[] | Poster | [] | Transformer-based models have achieved significant success in time series forecasting. Existing methods mainly model time series from limited or fixed scales, making it challenging to capture different characteristics spanning various scales. In this paper, we propose multi-scale transformers with adaptive pathways (Pathformer). The proposed Transformer integrates both temporal resolution and temporal distance for multi-scale modeling. Multi-scale division divides the time series into different temporal resolutions using patches of various sizes. Based on the division of each scale, dual attention is performed over these patches to capture global correlations and local details as temporal dependencies. We further enrich the multi-scale transformer with adaptive pathways, which adaptively adjust the multi-scale modeling process based on the varying temporal dynamics in the input time series, improving the prediction accuracy and generalization of Pathformer. Extensive experiments on nine real-world datasets demonstrate that Pathformer not only achieves state-of-the-art performance by surpassing all current models but also exhibits stronger generalization abilities under various transfer scenarios. | [] | [] | Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting | [
"Peng Chen",
"Yingying ZHANG",
"Yunyao Cheng",
"Yang Shu",
"Yihang Wang",
"Qingsong Wen",
"Bin Yang",
"Chenjuan Guo"
] | 2402.05956 | 17,938 | https://openreview.net/forum?id=lJkOCMP2aW |
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[] | Poster | [] | While pretrained large language models (LLMs) excel in understanding linguistic contexts, it is still an open question: Can LLMs extend their capabilities beyond linguistic contexts to non-linguistic information? This paper introduces VLAP, a novel approach that bridges vision encoders and language models through assignment prediction. Since the LLMs interpret and reason linguistic information from correlations between word embeddings, we harness the well-established word embeddings to map visual representations into language space. Specifically, we simultaneously assign the visual and text representations to a set of word embeddings within LLMs. We propose a new training objective, optimal transport-based assignment prediction, to enforce the consistency of word assignments for paired multimodal data. This allows frozen LLMs to ground their word embedding space in visual data and use their robust semantic taxonomy visually. Moreover, VLAP is memory- and parameter-efficient in that it trains only a single linear layer, and works without extra embedding space (e.g. learnable prototypes) for the assignment prediction. Experimental results show that VLAP achieves substantial improvements over the previous linear transformation-based methods across a range of vision-language tasks, including image captioning, visual question answering, and cross-modal retrieval. We also demonstrate the learned visual representations hold a semantic taxonomy of LLMs, making visual semantic arithmetic possible. | [] | [] | Bridging Vision and Language Spaces with Assignment Prediction | [
"Jungin Park",
"Jiyoung Lee",
"Kwanghoon Sohn"
] | 2404.09632 | 17,937 | https://openreview.net/forum?id=lK2V2E2MNv |
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[] | Poster | [] | The study of decoding visual neural information faces challenges in generalizing single-subject decoding models to multiple subjects, due to individual differences. Moreover, the limited availability of data from a single subject has a constraining impact on model performance. Although prior multi-subject decoding methods have made significant progress, they still suffer from several limitations, including difficulty in extracting global neural response features, linear scaling of model parameters with the number of subjects, and inadequate characterization of the relationship between neural responses of different subjects to various stimuli.To overcome these limitations, we propose a CLIP-guided Multi-sUbject visual neural information SEmantic Decoding (CLIP-MUSED) method. Our method consists of a Transformer-based feature extractor to effectively model global neural representations. It also incorporates learnable subject-specific tokens that facilitates the aggregation of multi-subject data without a linear increase of parameters. Additionally, we employ representational similarity analysis (RSA) to guide token representation learning based on the topological relationship of visual stimuli in the representation space of CLIP, enabling full characterization of the relationship between neural responses of different subjects under different stimuli. Finally, token representations are used for multi-subject semantic decoding. Our proposed method outperforms single-subject decoding methods and achieves state-of-the-art performance among the existing multi-subject methods on two fMRI datasets. Visualization results provide insights into the effectiveness of our proposed method. Code is available at https://github.com/CLIP-MUSED/CLIP-MUSED. | [] | [] | CLIP-MUSED: CLIP-Guided Multi-Subject Visual Neural Information Semantic Decoding | [
"Qiongyi Zhou",
"Changde Du",
"Shengpei Wang",
"Huiguang He"
] | 17,935 | https://openreview.net/forum?id=lKxL5zkssv |
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[] | Poster | [] | Large Language Models (LLMs) are highly capable of performing planning for long-horizon robotics tasks, yet existing methods require access to a pre-defined skill library (*e.g.* picking, placing, pulling, pushing, navigating). However, LLM planning does not address how to design or learn those behaviors, which remains challenging particularly in long-horizon settings. Furthermore, for many tasks of interest, the robot needs to be able to adjust its behavior in a fine-grained manner, requiring the agent to be capable of modifying *low-level* control actions. Can we instead use the internet-scale knowledge from LLMs for high-level policies, guiding reinforcement learning (RL) policies to efficiently solve robotic control tasks online without requiring a pre-determined set of skills? In this paper, we propose **Plan-Seq-Learn** (PSL): a modular approach that uses motion planning to bridge the gap between abstract language and learned low-level control for solving long-horizon robotics tasks from scratch. We demonstrate that PSL is capable of solving 20+ challenging single and multi-stage robotics tasks on four benchmarks at success rates of over 80% from raw visual input, out-performing language-based, classical, and end-to-end approaches. Video results and code at https://planseqlearn.github.io/ | [] | [] | Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks | [
"Murtaza Dalal",
"Tarun Chiruvolu",
"Devendra Singh Chaplot",
"Ruslan Salakhutdinov"
] | 18,096 | https://openreview.net/forum?id=hQVCCxQrYN |
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[] | Poster | [
"https://github.com/PKU-ML/non_neg"
] | Deep representations have shown promising performance when transferred to downstream tasks in a black-box manner. Yet, their inherent lack of interpretability remains a significant challenge, as these features are often opaque to human understanding. In this paper, we propose Non-negative Contrastive Learning (NCL), a renaissance of Non-negative Matrix Factorization (NMF) aimed at deriving interpretable features. The power of NCL lies in its enforcement of non-negativity constraints on features, reminiscent of NMF's capability to extract features that align closely with sample clusters. NCL not only aligns mathematically well with an NMF objective but also preserves NMF's interpretability attributes, resulting in a more sparse and disentangled representation compared to standard contrastive learning (CL). Theoretically, we establish guarantees on the identifiability and downstream generalization of NCL. Empirically, we show that these advantages enable NCL to outperform CL significantly on feature disentanglement, feature selection, as well as downstream classification tasks. | [] | [] | Non-negative Contrastive Learning | [
"Yifei Wang",
"Qi Zhang",
"Yaoyu Guo",
"Yisen Wang"
] | 2403.12459 | 17,933 | https://openreview.net/forum?id=lNCnZwcH5Z |
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[] | Poster | [] | This paper addresses the problem of anomaly detection in tabular data, which is usually implemented in an one-class classification setting where the training set only contains normal samples. Inspired by the success of masked image/language modeling in vision and natural language domains, we extend masked modeling methods to address this problem by capturing intrinsic correlations between features in training set. Thus, a sample deviate from such correlations is related to a high possibility of anomaly. To obtain multiple and diverse correlations, we propose a novel masking strategy which generates multiple masks by learning, and design a diversity loss to reduce the similarity of different masks. Extensive experiments show our method achieves state-of-the-art performance. We also discuss the interpretability from the perspective of each individual feature and correlations between features. | [] | [] | MCM: Masked Cell Modeling for Anomaly Detection in Tabular Data | [
"Jiaxin Yin",
"Yuanyuan Qiao",
"Zitang Zhou",
"Xiangchao Wang",
"Jie Yang"
] | 17,932 | https://openreview.net/forum?id=lNZJyEDxy4 |
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[] | Spotlight Poster | [] | Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detection. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on anomaly detection benchmarks yet computationally expensive. By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE). DTE estimates the distribution over diffusion time for a given input and uses the mode or mean of this distribution as the anomaly score. We derive an analytical form for this density and leverage a deep neural network to improve inference efficiency. Through empirical evaluations on the ADBench benchmark, we demonstrate that all diffusion-based anomaly detection methods perform competitively for both semi-supervised and unsupervised settings. Notably, DTE achieves orders of magnitude faster inference time than DDPM, while outperforming it on this benchmark. These results establish diffusion-based anomaly detection as a scalable alternative to traditional methods and recent deep-learning techniques for standard unsupervised and semi-supervised anomaly detection settings. | [] | [] | On Diffusion Modeling for Anomaly Detection | [
"Victor Livernoche",
"Vineet Jain",
"Yashar Hezaveh",
"Siamak Ravanbakhsh"
] | 2305.18593 | 17,930 | https://openreview.net/forum?id=lR3rk7ysXz |
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[] | Poster | [] | Conformal prediction (CP) can transform any model's output into prediction sets guaranteed to include (cover) the true label. CP requires exchangeability, a relaxation of the i.i.d. assumption, to obtain a valid distribution-free coverage guarantee. This makes it directly applicable to transductive node-classification. However, conventional CP cannot be applied in inductive settings due to the implicit shift in the (calibration) scores caused by message passing with the new nodes. We fix this issue for both cases of node and edge-exchangeable graphs, recovering the standard coverage guarantee without sacrificing statistical efficiency. We further prove that the guarantee holds independently of the prediction time, e.g. upon arrival of a new node/edge or at any subsequent moment. | [] | [] | Conformal Inductive Graph Neural Networks | [
"Soroush H. Zargarbashi",
"Aleksandar Bojchevski"
] | 18,084 | https://openreview.net/forum?id=homn1jOKI5 |
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[] | Poster | [] | Camouflaged object detection (COD) is the challenging task of identifying camouflaged objects visually blended into surroundings. Albeit achieving remarkable success, existing COD detectors still struggle to obtain precise results in some challenging cases. To handle this problem, we draw inspiration from the prey-vs-predator game that leads preys to develop better camouflage and predators to acquire more acute vision systems and develop algorithms from both the prey side and the predator side. On the prey side, we propose an adversarial training framework, Camouflageator, which introduces an auxiliary generator to generate more camouflaged objects that are harder for a COD method to detect. Camouflageator trains the generator and detector in an adversarial way such that the enhanced auxiliary generator helps produce a stronger detector. On the predator side, we introduce a novel COD method, called Internal Coherence and Edge Guidance (ICEG), which introduces a camouflaged feature coherence module to excavate the internal coherence of camouflaged objects, striving to obtain more complete segmentation results. Additionally, ICEG proposes a novel edge-guided separated calibration module to remove false predictions to avoid obtaining ambiguous boundaries. Extensive experiments show that ICEG outperforms existing COD detectors and Camouflageator is flexible to improve various COD detectors, including ICEG, which brings state-of-the-art COD performance. | [] | [] | Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects | [
"Chunming He",
"Kai Li",
"Yachao Zhang",
"Yulun Zhang",
"Chenyu You",
"Zhenhua Guo",
"Xiu Li",
"Martin Danelljan",
"Fisher Yu"
] | 2308.03166 | 18,079 | https://openreview.net/forum?id=hywpSoHwgX |
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[] | Poster | [] | Network embedding (NE) is a prominent techniques for network analysis that represents nodes as embeddings in a continuous vector space. We observe existing works all fall in the low-dimensional embedding space with two reasons: 1) it is empirically found that the increasing embedding dimension will cause the over-fitting of embedding models and the subsequent descent of model performance; 2) the overhead brought by high-dimensional embedding also makes a computing method seemingly impractical and worthless. In this paper, we explore a new NE paradigm whose embedding dimension goes exponentially high yet being very efficient and effective. Specifically, the node embeddings are represented as product quantum states that lie in a super high-dimensional (e.g. $2^{32}$-dim) quantum Hilbert space, with a carefully designed optimization approach to guarantee the robustness to work in different scenarios. In the experiments, we show diverse virtues of our methods, including but not limited to: the overwhelming performance on downstream tasks against conventional low-dimensional NE baselines with the similar amount of computing resources, the super high efficiency for a fixed low embedding dimension (e.g. 512) with less than 1/200 memory usage, the robustness when equipped with different objectives and sampling strategies as a fundamental tool for future NE research. As an unexplored topic in literature, the high-dimensional NE paradigm is demonstrated to be effective both experimentally and theoretically. | [] | [] | Node2ket: Efficient High-Dimensional Network Embedding in Quantum Hilbert Space | [
"Hao Xiong",
"Yehui Tang",
"Yunlin He",
"Wei Tan",
"Junchi Yan"
] | 17,929 | https://openreview.net/forum?id=lROh08eK6n |
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[] | Poster | [] | A prominent challenge of offline reinforcement learning (RL) is the issue of hidden confounding: unobserved variables may influence both the actions taken by the agent and the observed outcomes. Hidden confounding can compromise the validity of any causal conclusion drawn from data and presents a major obstacle to effective offline RL. In the present paper, we tackle the problem of hidden confounding in the nonidentifiable setting. We propose a definition of uncertainty due to hidden confounding bias, termed delphic uncertainty, which uses variation over world models compatible with the observations, and differentiate it from the well-known epistemic and aleatoric uncertainties. We derive a practical method for estimating the three types of uncertainties, and construct a pessimistic offline RL algorithm to account for them. Our method does not assume identifiability of the unobserved confounders, and attempts to reduce the amount of confounding bias. We demonstrate through extensive experiments and ablations the efficacy of our approach on a sepsis management benchmark, as well as on electronic health records. Our results suggest that nonidentifiable hidden confounding bias can be mitigated to improve offline RL solutions in practice. | [] | [] | Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding | [
"Alizée Pace",
"Hugo Yèche",
"Bernhard Schölkopf",
"Gunnar Ratsch",
"Guy Tennenholtz"
] | 2306.01157 | 17,928 | https://openreview.net/forum?id=lUYY2qsRTI |
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[] | Poster | [] | The ever-growing complexity of reinforcement learning (RL) tasks demands a distributed system to efficiently generate and process a massive amount of data. However, existing open-source libraries suffer from various limitations, which impede their practical use in challenging scenarios where large-scale training is necessary. In this paper, we present a novel abstraction on the dataflows of RLtraining, which unifies diverse RL training applications into a general framework. Following this abstraction, we develop a scalable, efficient, and extensible distributed RL system called ReaLly Scalable RL (SRL), which allows efficient and massively parallelized training and easy development of customized algorithms. Our evaluation shows that SRL outperforms existing academic libraries, reaching at most 21x higher training throughput in a distributed setting. On learning performance, beyond performing and scaling well on common RL benchmarks with different RL algorithms, SRL can reproduce the same solution in the challenging hide-and-seek environment as reported by OpenAI with up to 5x speedup in wallclock time. Notably, SRL is the first in the academic community to perform RL experiments at a large scale with over 15k CPU cores. SRL anonymous repository is available at: https://anonymous.4open.science/r/srl-1E45/. | [] | [] | SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores | [
"Zhiyu Mei",
"Wei Fu",
"Jiaxuan Gao",
"Guangju Wang",
"Huanchen Zhang",
"Yi Wu"
] | 2306.16688 | 17,927 | https://openreview.net/forum?id=lajn1iROCu |
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[] | Poster | [
"https://github.com/Sharut/CARE"
] | Self-supervised learning converts raw perceptual data such as images to a compact space where simple Euclidean distances measure meaningful variations in data. In this paper, we extend this formulation by adding additional geometric structure to the embedding space by enforcing transformations of input space to correspond to simple (i.e., linear) transformations of embedding space. Specifically, in the contrastive learning setting, we introduce an equivariance objective and theoretically prove that its minima force augmentations on input space to correspond to rotations on the spherical embedding space. We show that merely combining our equivariant loss with a non-collapse term results in non-trivial representations, without requiring invariance to data augmentations. Optimal performance is achieved by also encouraging approximate invariance, where input augmentations correspond to small rotations. Our method, CARE: Contrastive Augmentation-induced Rotational Equivariance, leads to improved performance on downstream tasks and ensures sensitivity in embedding space to important variations in data (e.g., color) that standard contrastive methods do not achieve. | [] | [] | Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning | [
"Sharut Gupta",
"Joshua Robinson",
"Derek Lim",
"Soledad Villar",
"Stefanie Jegelka"
] | 2306.13924 | 17,925 | https://openreview.net/forum?id=lgaFMvZHSJ |
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[] | Poster | [] | While molecular pre-training has shown great potential in enhancing drug discovery, the lack of a solid physical interpretation in current methods raises concerns about whether the learned representation truly captures the underlying explanatory factors in observed data, ultimately resulting in limited generalization and robustness. Although denoising methods offer a physical interpretation, their accuracy is often compromised by ad-hoc noise design, leading to inaccurate learned force fields. To address this limitation, this paper proposes a new method for molecular pre-training, called sliced denoising (SliDe), which is based on the classical mechanical intramolecular potential theory. SliDe utilizes a novel noise strategy that perturbs bond lengths, angles, and torsion angles to achieve better sampling over conformations. Additionally, it introduces a random slicing approach that circumvents the computationally expensive calculation of the Jacobian matrix, which is otherwise essential for estimating the force field. By aligning with physical principles, SliDe shows a 42\% improvement in the accuracy of estimated force fields compared to current state-of-the-art denoising methods, and thus outperforms traditional baselines on various molecular property prediction tasks. | [] | [] | Sliced Denoising: A Physics-Informed Molecular Pre-Training Method | [
"Yuyan Ni",
"Shikun Feng",
"Wei-Ying Ma",
"Zhi-Ming Ma",
"Yanyan Lan"
] | 2311.02124 | 17,924 | https://openreview.net/forum?id=liKkG1zcWq |
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[] | Poster | [] | With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present ViLMA (Video Language Model Assessment), a task-agnostic benchmark that places the assessment of fine-grained capabilities of these models on a firm footing. Task-based evaluations, while valuable, fail to capture the complexities and specific temporal aspects of moving images that VidLMs need to process. Through carefully curated counterfactuals, ViLMA offers a controlled evaluation suite that sheds light on the true potential of these models, as well as their performance gaps compared to human-level understanding. ViLMA also includes proficiency tests, which assess basic capabilities deemed essential to solving the main counterfactual tests. We show that current VidLMs’ grounding abilities are no better than those of vision-language models which use static images. This is especially striking once the performance on proficiency tests is factored in. Our benchmark serves as a catalyst for future research on VidLMs, helping to highlight areas that still need to be explored. | [] | [] | ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models | [
"Ilker Kesen",
"Andrea Pedrotti",
"Mustafa Dogan",
"Michele Cafagna",
"Emre Can Acikgoz",
"Letitia Parcalabescu",
"Iacer Calixto",
"Anette Frank",
"Albert Gatt",
"Aykut Erdem",
"Erkut Erdem"
] | 2311.07022 | 17,922 | https://openreview.net/forum?id=liuqDwmbQJ |
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[] | Poster | [] | Diffusion models for text-to-image (T2I) synthesis, such as Stable Diffusion (SD), have recently demonstrated exceptional capabilities for generating high-quality content. However, this progress has raised several concerns of potential misuse, particularly in creating copyrighted, prohibited, and restricted content, or NSFW (not safe for work) images. While efforts have been made to mitigate such problems, either by implementing a safety filter at the evaluation stage or by fine-tuning models to eliminate undesirable concepts or styles, the effectiveness of these safety measures in dealing with a wide range of prompts remains largely unexplored. In this work, we aim to investigate these safety mechanisms by proposing one novel concept retrieval algorithm for evaluation. We introduce Ring-A-Bell, a model-agnostic red-teaming scheme for T2I diffusion models, where the whole evaluation can be prepared in advance without prior knowledge of the target model.Specifically, Ring-A-Bell first performs concept extraction to obtain holistic representations for sensitive and inappropriate concepts. Subsequently, by leveraging the extracted concept, Ring-A-Bell automatically identifies problematic prompts for diffusion models with the corresponding generation of inappropriate content, allowing the user to assess the reliability of deployed safety mechanisms. Finally, we empirically validate our method by testing online services such as Midjourney and various methods of concept removal. Our results show that Ring-A-Bell, by manipulating safe prompting benchmarks, can transform prompts that were originally regarded as safe to evade existing safety mechanisms, thus revealing the defects of the so-called safety mechanisms which could practically lead to the generation of harmful contents. In essence, Ring-A-Bell could serve as a red-teaming tool to understand the limitations of deployed safety mechanisms and to explore the risk under plausible attacks. | [] | [] | Ring-A-Bell! How Reliable are Concept Removal Methods For Diffusion Models? | [
"Yu-Lin Tsai",
"Chia-Yi Hsu",
"Chulin Xie",
"Chih-Hsun Lin",
"Jia You Chen",
"Bo Li",
"Pin-Yu Chen",
"Chia-Mu Yu",
"Chun-Ying Huang"
] | 17,920 | https://openreview.net/forum?id=lm7MRcsFiS |
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[] | Poster | [] | It has been demonstrated that the training problem for a variety of (non) linear two-layer neural networks (such as two-layer perceptrons, convolutional networks, and self-attention) can be posed as equivalent convex optimization problems, with an induced regularizer which encourages low rank. However, this regularizer becomes prohibitively expensive to compute at moderate scales, impeding training convex neural networks. To this end, we propose applying the Burer-Monteiro factorization to convex neural networks, which for the first time enables a Burer-Monteiro perspective on neural networks with non-linearities. This factorization leads to an equivalent yet computationally tractable non-convex alternative with no spurious local minima. We develop a novel relative optimality bound of stationary points of the Burer-Monteiro factorization, providing verifiable conditions under which any stationary point is a global optimum. Further, for the first time, we show that linear self-attention with sufficiently many heads has no spurious local minima. Our experiments validate the novel relative optimality bound and the utility of the Burer-Monteiro factorization for scaling convex neural networks. | [] | [] | Scaling Convex Neural Networks with Burer-Monteiro Factorization | [
"Arda Sahiner",
"Tolga Ergen",
"Batu Ozturkler",
"John M. Pauly",
"Morteza Mardani",
"Mert Pilanci"
] | 18,051 | https://openreview.net/forum?id=ikmuHqugN7 |
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[] | Poster | [] | In this paper, we address causal reasoning in multivariate time series data generated by stochastic processes. Traditional approaches are largely restricted to static settings, ignoring the continuity and emission of variations across time. In contrast, we propose a learning paradigm that directly establishes causation between \emph{events} in the course of time. We present two key lemmas to compute causal contributions and frame them as reinforcement learning problems. Our approach offers formal and computational tools for uncovering and quantifying causal relationships in diffusion processes, subsuming various important settings such as discrete-time Markov decision processes. Finally, in fairly intricate experiments and through sheer learning, our framework reveals and quantifies causal links, which otherwise seem inexplicable. | [] | [] | A Dynamical View of the Question of Why | [
"Mehdi Fatemi",
"Sindhu C. M. Gowda"
] | 2402.10240 | 17,917 | https://openreview.net/forum?id=lrQlLqQase |
|
[] | Poster | [] | In this work, we present efficient modulation, a novel design for efficient vision networks. We revisit the modulation mechanism, which operates input through convolutional context modeling and feature projection layers, and fuses features via element-wise multiplication and an MLP block. We demonstrate that the abstracted modulation mechanism is particularly well suited for efficient networks and further tailor the modulation design by proposing the efficient modulation (EfficientMod) block, which is considered the essential building block for our networks. Bene- fiting from the prominent representational ability of modulation mechanism and the efficiency of efficient modulation design, our network can accomplish better accuracy-efficiency trade-offs and set new state-of-the-art performance for efficient networks. When integrating EfficientMod block with the vanilla self-attention block, we obtain the hybrid architecture and further improve the performance without sacrificing the efficiency. We carry out comprehensive experiments to verify EfficientMod’s performance. With fewer parameters, our EfficientMod-s performs 0.6 top-1 accuracy better than the prior state-of-the-art approach EfficientFormerV2-s2 without any training tricks and is 25% faster on GPU. Additionally, our method presents a notable improvement in downstream tasks, outperforming EfficientFormerV2-s by 3.6 mIoU on the ADE20K benchmark. Codes and checkpoints are available in the supplementary material. | [] | [] | Efficient Modulation for Vision Networks | [
"Xu Ma",
"Xiyang Dai",
"Jianwei Yang",
"Bin Xiao",
"Yinpeng Chen",
"Yun Fu",
"Lu Yuan"
] | 2403.19963 | 18,048 | https://openreview.net/forum?id=ip5LHJs6QX |
|
[] | Poster | [] | Modern neural networks are known to give overconfident predictions for out-of-distribution inputs when deployed in the open world. It is common practice to leverage a surrogate outlier dataset to regularize the model during training, and recent studies emphasize the role of uncertainty in designing the sampling strategy for outlier datasets. However, the OOD samples selected solely based on predictive uncertainty can be biased towards certain types, which may fail to capture the full outlier distribution. In this work, we empirically show that diversity is critical in sampling outliers for OOD detection performance. Motivated by the observation, we propose a straightforward and novel sampling strategy named DOS (Diverse Outlier Sampling) to select diverse and informative outliers. Specifically, we cluster the normalized features at each iteration, and the most informative outlier from each cluster is selected for model training with absent category loss. With DOS, the sampled outliers efficiently shape a globally compact decision boundary between ID and OOD data. Extensive experiments demonstrate the superiority of DOS, reducing the average FPR95 by up to 25.79% on CIFAR-100 with TI-300K. | [] | [] | DOS: Diverse Outlier Sampling for Out-of-Distribution Detection | [
"Wenyu Jiang",
"Hao Cheng",
"MingCai Chen",
"Chongjun Wang",
"Hongxin Wei"
] | 2306.02031 | 18,047 | https://openreview.net/forum?id=iriEqxFB4y |
|
[] | Spotlight Poster | [] | We study a strategic variant of the multi-armed bandit problem, which we coin the strategic click-bandit. This model is motivated by applications in online recommendation where the choice of recommended items depends on both the click-through rates and the post-click rewards. Like in classical bandits, rewards follow a fixed unknown distribution. However, we assume that the click-rate of each arm is chosen strategically by the arm (e.g., a host on Airbnb) in order to maximize the number of times it gets clicked. The algorithm designer does not know the post-click rewards nor the arms' actions (i.e., strategically chosen click-rates) in advance, and must learn both values over time. To solve this problem, we design an incentive-aware learning algorithm, UCB-S, which achieves two goals simultaneously: (a) incentivizing desirable arm behavior under uncertainty; (b) minimizing regret by learning unknown parameters. We approximately characterize all Nash equilibria of the arms under UCB-S and show a $\tilde{\mathcal{O}} (\sqrt{KT})$ regret bound uniformly in every equilibrium. We also show that incentive-unaware algorithms generally fail to achieve low regret in the strategic click-bandit. Finally, we support our theoretical results by simulations of strategic arm behavior which confirm the effectiveness and robustness of our proposed incentive design. | [] | [] | Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation | [
"Thomas Kleine Buening",
"Aadirupa Saha",
"Christos Dimitrakakis",
"Haifeng Xu"
] | 2311.15647 | 17,915 | https://openreview.net/forum?id=lsxeNvYqCj |
|
[] | Poster | [
"https://github.com/mazurowski-lab/intrinsic-properties"
] | This paper investigates discrepancies in how neural networks learn from different imaging domains, which are commonly overlooked when adopting computer vision techniques from the domain of natural images to other specialized domains such as medical images. Recent works have found that the generalization error of a trained network typically increases with the intrinsic dimension ($d_{data}$) of its training set. Yet, the steepness of this relationship varies significantly between medical (radiological) and natural imaging domains, with no existing theoretical explanation. We address this gap in knowledge by establishing and empirically validating a generalization scaling law with respect to $d_{data}$, and propose that the substantial scaling discrepancy between the two considered domains may be at least partially attributed to the higher intrinsic ``label sharpness'' ($K_\mathcal{F}$) of medical imaging datasets, a metric which we propose. Next, we demonstrate an additional benefit of measuring the label sharpness of a training set: it is negatively correlated with the trained model's adversarial robustness, which notably leads to models for medical images having a substantially higher vulnerability to adversarial attack. Finally, we extend our $d_{data}$ formalism to the related metric of learned representation intrinsic dimension ($d_{repr}$), derive a generalization scaling law with respect to $d_{repr}$, and show that $d_{data}$ serves as an upper bound for $d_{repr}$. Our theoretical results are supported by thorough experiments with six models and eleven natural and medical imaging datasets over a range of training set sizes. Our findings offer insights into the influence of intrinsic dataset properties on generalization, representation learning, and robustness in deep neural networks. *Code link: https://github.com/mazurowski-lab/intrinsic-properties* | [] | [] | The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images | [
"Nicholas Konz",
"Maciej A Mazurowski"
] | 2401.08865 | 18,044 | https://openreview.net/forum?id=ixP76Y33y1 |
|
[] | Poster | [] | Large pre-trained models have enabled significant advances in machine learning and served as foundation components.Model fusion methods, such as task arithmetic, have been proven to be powerful and scalable to incorporate fine-tuned weights from different tasks into a multi-task model. However, efficiently fine-tuning large pre-trained models on multiple downstream tasks remains challenging, leading to inefficient multi-task model fusion.In this work, we propose a novel method to improve multi-task fusion for parameter-efficient fine-tuning techniques like LoRA fine-tuning.Specifically, our approach partially linearizes only the adapter modules and applies task arithmetic over the linearized adapters.This allows us to leverage the the advantages of model fusion over linearized fine-tuning, while still performing fine-tuning and inference efficiently.We demonstrate that our partial linearization technique enables a more effective fusion of multiple tasks into a single model, outperforming standard adapter tuning and task arithmetic alone.Experimental results demonstrate the capabilities of our proposed partial linearization technique to effectively construct unified multi-task models via the fusion of fine-tuned task vectors. We evaluate performance over an increasing number of tasks and find that our approach outperforms standard parameter-efficient fine-tuning techniques. The results highlight the benefits of partial linearization for scalable and efficient multi-task model fusion. | [] | [] | Parameter-Efficient Multi-Task Model Fusion with Partial Linearization | [
"Anke Tang",
"Li Shen",
"Yong Luo",
"Yibing Zhan",
"Han Hu",
"Bo Du",
"Yixin Chen",
"Dacheng Tao"
] | 18,043 | https://openreview.net/forum?id=iynRvVVAmH |
||
[] | Poster | [
"https://github.com/haochenglouis/RobustTSF"
] | Time series forecasting is an important and forefront task whose techniques have been applied to electricity forecasting, trajectory prediction, labor planning, etc. However, most of time series forecasting techniques assume that the training data is clean without anomalies. This assumption is unrealistic since the collected time series data can be contaminated in practice. The forecasting model will be inferior if it is directly trained by time series with anomalies. Thus it is essential to develop methods to automatically learn a robust forecasting model from the contaminated data. In this paper, we first statistically define three types of anomalies, then theoretically and experimentally analyze the loss robustness and sample robustness when these anomalies exist. Based on our analyses, we propose a simple and efficient algorithm to learn a robust forecasting model. Extensive experiments show that our method is highly robust and outperforms all existing approaches. | [] | [] | RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies | [
"Hao Cheng",
"Qingsong Wen",
"Yang Liu",
"Liang Sun"
] | 2402.02032 | 17,914 | https://openreview.net/forum?id=ltZ9ianMth |
|
[] | Poster | [] | The family of probabilistic values, axiomatically-grounded and proposed in cooperative game theory, has recently received much attention in data valuation. However, it is often computationally expensive to compute exactly (exponential w.r.t. $N$, the number of data being valuated). Existing generic estimators cost $O(\frac{N^2}{\epsilon^2}\log\frac{N}{\delta})$ utility evaluations to achieve an ($\epsilon$, $\delta$)-approximation under the 2-norm, while faster estimators have been developed recently for special cases (e.g., empirically for the Shapley value and theoretically for the Banzhaf value). In this work, based on a connection between probabilistic values and least square regressions, we propose two generic estimators for the whole family of probabilistic values that both cost $O(\frac{N}{\epsilon^2}\log\frac{N}{\delta})$ utility evaluations, largely extending the scope of this currently best complexity bound. Moreover, we show that each distributional value, proposed by Ghorbani et al. (2020) to alleviate the inconsistency of probabilistic values when using distinct databases, can also be cast as optimizing a similar least square regression. This observation makes it the first-time theoretically-grounded to train value estimators such that the distributional value of each unseen data point can be evaluated in a single forward pass. Our experiments verify the faster convergence of our proposed estimators, and demonstrate the effectiveness at learning distributional values. | [] | [] | Faster Approximation of Probabilistic and Distributional Values via Least Squares | [
"Weida Li",
"Yaoliang Yu"
] | 17,913 | https://openreview.net/forum?id=lvSMIsztka |
||
[] | Poster | [] | Real-world deployment of machine learning models is challenging because data evolves over time. While no model can work when data evolves in an arbitrary fashion, if there is some pattern to these changes, we might be able to design methods to address it. This paper addresses situations when data evolves gradually. We introduce a time-varying propensity score that can detect gradual shifts in the distribution of data which allows us to selectively sample past data to update the model---not just similar data from the past like that of a standard propensity score but also data that evolved in a similar fashion in the past. The time-varying propensity score is quite general: we demonstrate different ways of implementing it and evaluate it on a variety of problems ranging from supervised learning (e.g., image classification problems) where data undergoes a sequence of gradual shifts, to reinforcement learning tasks (e.g., robotic manipulation and continuous control) where data shifts as the policy or the task changes. | [] | [] | Time-Varying Propensity Score to Bridge the Gap between the Past and Present | [
"Rasool Fakoor",
"Jonas Mueller",
"Zachary Chase Lipton",
"Pratik Chaudhari",
"Alex Smola"
] | 2210.01422 | 17,912 | https://openreview.net/forum?id=m0x0rv6Iwm |
|
[] | Poster | [] | Recent claims about the impressive abilities of large language models (LLMs) are often supported by evaluating publicly available benchmarks. Since LLMs train on wide swaths of the internet, this practice raises concerns of data contamination, i.e., evaluating on examples that are explicitly or implicitly included in the training data. Data contamination remains notoriously challenging to measure and mitigate, even with partial attempts like controlled experimentation of training data, canary strings, or embedding similarities. In this work, we conduct the first thorough longitudinal analysis of data contamination in LLMs by using the natural experiment of training cutoffs in GPT models to look at benchmarks released over time.Specifically, we consider two code/mathematical problem-solving datasets, Codeforces and Project Euler, and find statistically significant trends among LLM pass rate vs. GitHub popularity and release date that provide strong evidence of contamination. By open-sourcing our dataset, raw results, and evaluation framework, our work paves the way for rigorous analyses of data contamination in modern models. We conclude with a discussion of best practices and future steps for publicly releasing benchmark in the age of LLMs which train on webscale data. | [] | [] | To the Cutoff... and Beyond? A Longitudinal Perspective on LLM Data Contamination | [
"Manley Roberts",
"Himanshu Thakur",
"Christine Herlihy",
"Colin White",
"Samuel Dooley"
] | 17,911 | https://openreview.net/forum?id=m2NVG4Htxs |
||
[] | Poster | [] | Large Language Models (LLMs) have made unprecedented breakthroughs, yet their increasing integration into everyday life might raise societal risks due to generated unethical content. Despite extensive study on specific issues like bias, the intrinsic values of LLMs remain largely unexplored from a moral philosophy perspective. This work delves into ethical values utilizing Moral Foundation Theory. Moving beyond conventional discriminative evaluations with poor reliability, we propose DeNEVIL, a novel prompt generation algorithm tailored to dynamically exploit LLMs’ value vulnerabilities and elicit the violation of ethics in a generative manner, revealing their underlying value inclinations. On such a basis, we construct MoralPrompt, a high-quality dataset comprising 2,397 prompts covering 500+ value principles, and then benchmark the intrinsic values across a spectrum of LLMs. We discovered that most models are essentially misaligned, necessitating further ethical value alignment. In response, we develop VILMO, an in-context alignment method that substantially enhances the value compliance of LLM outputs by learning to generate appropriate value instructions, outperforming existing competitors. Our methods are suitable for black-box and open-source models, offering a promising initial step in studying the ethical values of LLMs. | [] | [] | DENEVIL: TOWARDS DECIPHERING AND NAVIGATING THE ETHICAL VALUES OF LARGE LANGUAGE MODELS VIA INSTRUCTION LEARNING | [
"Shitong Duan",
"Xiaoyuan Yi",
"Peng Zhang",
"Tun Lu",
"Xing Xie",
"Ning Gu"
] | 17,910 | https://openreview.net/forum?id=m3RRWWFaVe |
||
[] | Poster | [] | Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy learning.To tackle this task and empower robots with the ability to foresee the future, we propose a sample and computation-efficient model, named Seer, by inflating the pretrained text-to-image (T2I) stable diffusion models along the temporal axis. We enhance the U-Net and language conditioning model by incorporating computation-efficient spatial-temporal attention. Furthermore, we introduce a novel Frame Sequential Text Decomposer module that dissects a sentence's global instruction into temporally aligned sub-instructions, ensuring precise integration into each frame of generation. Our framework allows us to effectively leverage the extensive prior knowledge embedded in pretrained T2I models across the frames. With the adaptable-designed architecture, Seer makes it possible to generate high-fidelity, coherent, and instruction-aligned video frames by fine-tuning a few layers on a small amount of data. The experimental results on Something Something V2 (SSv2), Bridgedata and EpicKitchens-100 datasets demonstrate our superior video prediction performance with around 480-GPU hours versus CogVideo with over 12,480-GPU hours: achieving the 31\% FVD improvement compared to the current SOTA model on SSv2 and 83.7\% average preference in the human evaluation. Our project is available at https://seervideodiffusion.github.io/ | [] | [] | Seer: Language Instructed Video Prediction with Latent Diffusion Models | [
"Xianfan Gu",
"Chuan Wen",
"Weirui Ye",
"Jiaming Song",
"Yang Gao"
] | 2303.14897 | 17,739 | https://openreview.net/forum?id=qHGgNyQk31 |
|
[] | Spotlight Poster | [
"https://github.com/chunmeifeng/SPRC"
] | Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption. Most existing CIR models adopt the late-fusion strategy to combine visual and language features. Besides, several approaches have also been suggested to generate a pseudo-word token from the reference image, which is further integrated into the relative caption for CIR. However, these pseudo-word-based prompting methods have limitations when target image encompasses complex changes on reference image, e.g., object removal and attribute modification. In this work, we demonstrate that learning an appropriate sentence-level prompt for the relative caption (SPRC) is sufficient for achieving effective composed image retrieval. Instead of relying on pseudo- word-based prompts, we propose to leverage pretrained V-L models, e.g., BLIP-2, to generate sentence-level prompts. By concatenating the learned sentence-level prompt with the relative caption, one can readily use existing text-based image retrieval models to enhance CIR performance. Furthermore, we introduce both image-text contrastive loss and text prompt alignment loss to enforce the learning of suitable sentence-level prompts. Experiments show that our proposed method performs favorably against the state-of-the-art CIR methods on the Fashion-IQ and CIRR datasets. The source code and pretrained model will be publicly available. | [] | [] | Sentence-level Prompts Benefit Composed Image Retrieval | [
"Yang bai",
"Xinxing Xu",
"Yong Liu",
"Salman Khan",
"Fahad Khan",
"Wangmeng Zuo",
"Rick Siow Mong Goh",
"Chun-Mei Feng"
] | 2310.05473 | 17,909 | https://openreview.net/forum?id=m3ch3kJL7q |
|
[] | Poster | [] | Human beings can make adaptive decisions in a preparatory manner, i.e., by making preparations in advance, which offers significant advantages in scenarios where both online and offline experiences are expensive and limited. Meanwhile, current reinforcement learning methods commonly rely on numerous environment interactions but hardly obtain generalizable policies. In this paper, we introduce the idea of \textit{rehearsal} into policy optimization, where the agent plans for all possible outcomes in mind and acts adaptively according to actual responses from the environment. To effectively rehearse, we propose ReDM, an algorithm that generates a diverse and eligible set of dynamics models and then rehearse the policy via adaptive training on the generated model set. Rehearsal enables the policy to make decision plans for various hypothetical dynamics and to naturally generalize to previously unseen environments. Our experimental results demonstrate that ReDM is capable of learning a valid policy solely through rehearsal, even with \emph{zero} interaction data. We further extend ReDM to scenarios where limited or mismatched interaction data is available, and our experimental results reveal that ReDM produces high-performing policies compared to other offline RL baselines. | [] | [] | Policy Rehearsing: Training Generalizable Policies for Reinforcement Learning | [
"Chengxing Jia",
"Chenxiao Gao",
"Hao Yin",
"Fuxiang Zhang",
"Xiong-Hui Chen",
"Tian Xu",
"Lei Yuan",
"Zongzhang Zhang",
"Zhi-Hua Zhou",
"Yang Yu"
] | 17,908 | https://openreview.net/forum?id=m3xVPaZp6Z |
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