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[] | Poster | [] | The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM’s adversarial robustness. This paper proposes an efficient tool to audit the LLM’s adversarial robustness via a prompt-based adversarial attack (PromptAttack). PromptAttack converts adversarial textual attacks into an attack prompt that can cause the victim LLM to output the adversarial sample to fool itself. The attack prompt is composed of three important components: (1) original input (OI) including the original sample and its ground-truth label, (2) attack objective (AO) illustrating a task description of generating a new sample that can fool itself without changing the semantic meaning, and (3) attack guidance (AG) containing the perturbation instructions to guide the LLM on how to complete the task by perturbing the original sample at character, word, and sentence levels, respectively. Besides, we use a fidelity filter to ensure that PromptAttack maintains the original semantic meanings of the adversarial examples. Further, we enhance the attack power of PromptAttack by ensembling adversarial examples at different perturbation levels. Comprehensive empirical results using Llama2 and GPT-3.5 validate that PromptAttack consistently yields a much higher attack success rate compared to AdvGLUE and AdvGLUE++. Interesting findings include that a simple emoji can easily mislead GPT-3.5 to make wrong predictions. Our source code is available at Anonymous GitHub. | [] | [] | An LLM can Fool Itself: A Prompt-Based Adversarial Attack | [
"Xilie Xu",
"Keyi Kong",
"Ning Liu",
"Lizhen Cui",
"Di Wang",
"Jingfeng Zhang",
"Mohan Kankanhalli"
] | 2310.13345 | 18,503 | https://openreview.net/forum?id=VVgGbB9TNV |
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[] | Poster | [] | Maximum mean discrepancy (MMD) flows suffer from high computational costs in large scale computations.In this paper, we show that MMD flows with Riesz kernels $K(x,y) = - \|x-y\|^r$, $r \in (0,2)$have exceptional properties which allow their efficient computation.We prove that the MMD of Riesz kernels, which is also known as energy distance, coincides with the MMD of their sliced version.As a consequence, the computation of gradients of MMDs can be performed in the one-dimensional setting.Here, for $r=1$, a simple sorting algorithm can be applied to reduce the complexityfrom $O(MN+N^2)$ to $O((M+N)\log(M+N))$ for two measures with $M$ and $N$ support points.As another interesting follow-up result, the MMD of compactly supported measurescan be estimated from above and below by the Wasserstein-1 distance.For the implementations we approximate the gradient of the sliced MMD by using only a finite number $P$ of slices. We show that the resulting error has complexity \smash{$O(\sqrt{d/P})$}, where $d$ is the data dimension. These results enable us to train generative models by approximating MMD gradient flows by neural networks evenfor image applications. We demonstrate the efficiency of our model by image generation on MNIST, FashionMNIST and CIFAR10. | [] | [] | Generative Sliced MMD Flows with Riesz Kernels | [
"Johannes Hertrich",
"Christian Wald",
"Fabian Altekrüger",
"Paul Hagemann"
] | 2305.11463 | 18,500 | https://openreview.net/forum?id=VdkGRV1vcf |
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[] | Poster | [] | Existing video-language pre-training methods primarily focus on instance-level alignment between video clips and captions via global contrastive learning but neglect rich fine-grained local information in both videos and text, which is of importance to downstream tasks requiring temporal localization and semantic reasoning. A powerful model is expected to be capable of capturing region-object correspondences and recognizing scene changes in a video clip, reflecting spatial and temporal granularity respectively. To strengthen model's understanding of such fine-grained information, we propose a simple yet effective video-language modeling framework, S-ViLM, based on intrinsic structures of these two modalities. It includes two novel designs, inter-clip spatial grounding and intra-clip temporal grouping, to promote learning region-object alignment and temporal-aware features simultaneously. Comprehensive evaluations demonstrate that S-ViLM performs favorably against existing approaches in learning more expressive representations.Specifically, S-ViLM surpasses the state-of-the-art methods substantially on four representative downstream tasks, covering text-video retrieval, video question answering, video action recognition and temporal action localization. | [] | [] | Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding | [
"Yuanhao Xiong",
"Long Zhao",
"Boqing Gong",
"Ming-Hsuan Yang",
"Florian Schroff",
"Ting Liu",
"Cho-Jui Hsieh",
"Liangzhe Yuan"
] | 2303.16341 | 19,422 | https://openreview.net/forum?id=5dlfiJIXoh |
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[] | Poster | [] | Modern learning-based approaches to 3D-aware image synthesis achieve high photorealism and 3D-consistent viewpoint changes for the generated images. Existing approaches represent instances in a shared canonical space. However, for in-the-wild datasets a shared canonical system can be difficult to define or might not even exist. In this work, we instead model instances in view space, alleviating the need for posed images and learned camera distributions. We find that in this setting, existing GAN-based methods are prone to generating flat geometry and struggle with distribution coverage. We hence propose WildFusion, a new approach to 3D-aware image synthesis based on latent diffusion models (LDMs). We first train an autoencoder that infers a compressed latent representation, which additionallycaptures the images’ underlying 3D structure and enables not only reconstruction but also novel view synthesis. To learn a faithful 3D representation, we leverage cues from monocular depth prediction. Then, we train a diffusion model in the 3D-aware latent space, thereby enabling synthesis of high-quality 3D-consistent image samples, outperforming recent state-of-the-art GAN-based methods. Importantly,our 3D-aware LDM is trained without any direct supervision from multiview images or 3D geometry and does not require posed images or learned pose or camera distributions. It directly learns a 3D representation without relying on canonical camera coordinates. This opens up promising research avenues for scalable 3D-aware image synthesis and 3D content creation from in-the-wild image data. | [] | [] | WildFusion: Learning 3D-Aware Latent Diffusion Models in View Space | [
"Katja Schwarz",
"Seung Wook Kim",
"Jun Gao",
"Sanja Fidler",
"Andreas Geiger",
"Karsten Kreis"
] | 2311.13570 | 18,499 | https://openreview.net/forum?id=VdwVOREDZM |
|
[] | Poster | [
"https://github.com/MGitHubL/Deep-Temporal-Graph-Clustering"
] | Deep graph clustering has recently received significant attention due to its ability to enhance the representation learning capabilities of models in unsupervised scenarios. Nevertheless, deep clustering for temporal graphs, which could capture crucial dynamic interaction information, has not been fully explored. It means that in many clustering-oriented real-world scenarios, temporal graphs can only be processed as static graphs. This not only causes the loss of dynamic information but also triggers huge computational consumption. To solve the problem, we propose a general framework for deep Temporal Graph Clustering called TGC, which adjusts deep clustering techniques (clustering assignment distribution and adjacency matrix reconstruction) to suit the interaction sequence-based batch-processing pattern of temporal graphs. In addition, we discuss differences between temporal graph clustering and existing static graph clustering from several levels. To verify the superiority of the proposed framework TGC, we conduct extensive experiments. The experimental results show that temporal graph clustering enables more flexibility in finding a balance between time and space requirements, and our framework can effectively improve the performance of existing temporal graph learning methods. Our code is included in the supplementary material and will be released after publication. | [] | [] | Deep Temporal Graph Clustering | [
"Meng Liu",
"Yue Liu",
"KE LIANG",
"Wenxuan Tu",
"Siwei Wang",
"sihang zhou",
"Xinwang Liu"
] | 2305.10738 | 18,498 | https://openreview.net/forum?id=ViNe1fjGME |
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[] | Poster | [] | Large web-crawled multimodal datasets have powered a slew of new methods for learning general-purpose visual representations, advancing the state of the art in computer vision and revolutionizing zero- and few-shot recognition. One crucial decision facing practitioners is how, if at all, to curate these ever-larger datasets. For example, the creators of the LAION-5B dataset chose to retain only image-caption pairs whose CLIP similarity score exceeded a designated threshold. In this paper, we propose a new state-of-the-art data filtering approach motivated by our observation that nearly $40\%$ of LAION's images contain text that overlaps significantly with the caption. Intuitively, such data could be wasteful as it incentivizes models to perform optical character recognition rather than learning visual features. However, naively removing all such data could also be wasteful, as it throws away images that contain visual features (in addition to overlapping text). Our simple and scalable approach, T-MARS (Text Masking and Re-Scoring), filters out only those pairs where the text dominates the remaining visual features---by first masking out the text and then filtering out those with a low CLIP similarity score of the masked image with original captions. Experimentally, T-MARS is the top ranked approach on Imagenet at ``medium scale'' of DataComp (a data filtering benchmark), and outperforms CLIP filtering by a margin of $6.5\%$ on ImageNet and $4.7\%$ on VTAB. Additionally, we show that the accuracy gains enjoyed by T-MARS linearly increase as data and compute are scaled exponentially. | [] | [] | T-MARS: Improving Visual Representations by Circumventing Text Feature Learning | [
"Pratyush Maini",
"Sachin Goyal",
"Zachary Chase Lipton",
"J Zico Kolter",
"Aditi Raghunathan"
] | 18,497 | https://openreview.net/forum?id=ViPtjIVzUw |
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[] | Poster | [] | Fine-tuning is essential to adapting pre-trained large language models to downstream applications. With the increasing popularity of LLM-enabled applications, fine-tuning has been performed intensively worldwide, incurring a tremendous amount of computing costs that correspond to big carbon footprint and environmental impact. Mitigating such environmental impact directly correlates to reducing the fine-tuning FLOPs. Existing fine-tuning schemes focus on either saving memory or reducing the overhead of computing weight updates, but cannot achieve sufficient FLOPs reduction due to their ignorance of the training cost in backpropagation. To address this limitation, in this paper we present GreenTrainer, a new technique that minimizes the FLOPs of LLM fine-tuning via adaptive backpropagation, which adaptively selects the most appropriate set of LLM tensors for fine-tuning based on their importance and backpropagation cost in training. Experiment results show that GreenTrainer can save up to 64\% training FLOPs compared to full fine-tuning, without any noticeable accuracy loss. Compared to the existing schemes such as Prefix Tuning and LoRA, GreenTrainer can achieve up to 4\% improvement of model accuracy, with on-par FLOPs reduction. | [] | [] | Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation | [
"Kai Huang",
"Hanyun Yin",
"Heng Huang",
"Wei Gao"
] | 2309.13192 | 18,496 | https://openreview.net/forum?id=Vja3ecieXY |
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[] | Poster | [
"https://github.com/cambridge-mlg/RECOMBINER"
] | COMpression with Bayesian Implicit NEural Representations (COMBINER) is a recent data compression method that addresses a key inefficiency of previous Implicit Neural Representation (INR)-based approaches: it avoids quantization and enables direct optimization of the rate-distortion performance. However, COMBINER still has significant limitations: 1) it uses factorized priors and posterior approximations that lack flexibility; 2) it cannot effectively adapt to local deviations from global patterns in the data; and 3) its performance can be susceptible to modeling choices and the variational parameters' initializations. Our proposed method, Robust and Enhanced COMBINER (RECOMBINER), addresses these issues by 1) enriching the variational approximation while maintaining its computational cost via a linear reparameterization of the INR weights, 2) augmenting our INRs with learnable positional encodings that enable them to adapt to local details and 3) splitting high-resolution data into patches to increase robustness and utilizing expressive hierarchical priors to capture dependency across patches. We conduct extensive experiments across several data modalities, showcasing that RECOMBINER achieves competitive results with the best INR-based methods and even outperforms autoencoder-based codecs on low-resolution images at low bitrates. | [] | [] | RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations | [
"Jiajun He",
"Gergely Flamich",
"Zongyu Guo",
"José Miguel Hernández-Lobato"
] | 2309.17182 | 18,495 | https://openreview.net/forum?id=VkWbxFrCC8 |
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[] | Spotlight Poster | [] | Solving a linear system ${\bf Ax}={\bf b}$ is a fundamental scientific computing primitive, and numerous solvers and preconditioners have been developed. These come with parameters whose optimal values depend on the system being solved but are often impossible or too expensive to identify; thus in practice sub-optimal heuristics are used instead. We consider the common setting in which many related linear systems are solved, e.g. during a single numerical simulation. In this scenario, can we sequentially choose parameters that attain a near-optimal overall number of iterations, without extra matrix computations? We answer in the affirmative for Successive Over-Relaxation~(SOR), a standard solver whose parameter $\omega$ has a strong impact on its runtime. For this method, we prove that a bandit algorithm—using only the number of iterations as feedback—can select parameters for a sequence of instances such that the overall cost is almost as good as that the best fixed $\omega$ would have obtained. Furthermore, when given additional structural information, we show that a {\em contextual} bandit method approaches the performance of the {\em instance-optimal} policy, which selects the best $\omega$ for each instance. Our work provides the first learning-theoretic treatment of high-precision linear system solvers and the first end-to-end guarantees for data-driven scientific computing, demonstrating theoretically the potential to speed up numerical methods using well-understood learning algorithms. | [] | [] | Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances | [
"Mikhail Khodak",
"Edmond Chow",
"Maria Florina Balcan",
"Ameet Talwalkar"
] | 2310.02246 | 19,409 | https://openreview.net/forum?id=5t57omGVMw |
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[] | Spotlight Poster | [] | When a small number of poisoned samples are injected into the training dataset of a deep neural network, the network can be induced to exhibit malicious behavior during inferences, which poses potential threats to real-world applications. While they have been intensively studied in classification, backdoor attacks on semantic segmentation have been largely overlooked. Unlike classification, semantic segmentation aims to classify every pixel within a given image. In this work, we explore backdoor attacks on segmentation models to misclassify all pixels of a victim class by injecting a specific trigger on non-victim pixels during inferences, which is dubbed Influencer Backdoor Attack (IBA). IBA is expected to maintain the classification accuracy of non-victim pixels and mislead classifications of all victim pixels in every single inference and could be easily applied to real-world scenes. Based on the context aggregation ability of segmentation models, we proposed a simple, yet effective, Nearest-Neighbor trigger injection strategy. We also introduce an innovative Pixel Random Labeling strategy which maintains optimal performance even when the trigger is placed far from the victim pixels. Our extensive experiments reveal that current segmentation models do suffer from backdoor attacks, demonstrate IBA real-world applicability, and show that our proposed techniques can further increase attack performance. | [] | [] | Influencer Backdoor Attack on Semantic Segmentation | [
"Haoheng Lan",
"Jindong Gu",
"Philip Torr",
"Hengshuang Zhao"
] | 2303.12054 | 18,494 | https://openreview.net/forum?id=VmGRoNDQgJ |
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[] | Poster | [] | Modern deep learning requires large volumes of data, which could contain sensitive or private information that cannot be leaked. Recent work has shown for homogeneous neural networks a large portion of this training data could be reconstructed with only access to the trained network parameters. While the attack was shown to work empirically, there exists little formal understanding of its effective regime which datapoints are susceptible to reconstruction. In this work, we first build a stronger version of the dataset reconstruction attack and show how it can provably recover the \emph{entire training set} in the infinite width regime. We then empirically study the characteristics of this attack on two-layer networks and reveal that its success heavily depends on deviations from the frozen infinite-width Neural Tangent Kernel limit. Next, we study the nature of easily-reconstructed images. We show that both theoretically and empirically, reconstructed images tend to ``outliers'' in the dataset, and that these reconstruction attacks can be used for \textit{dataset distillation}, that is, we can retrain on reconstructed images and obtain high predictive accuracy. | [] | [] | Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation | [
"Noel Loo",
"Ramin Hasani",
"Mathias Lechner",
"Alexander Amini",
"Daniela Rus"
] | 2302.01428 | 18,493 | https://openreview.net/forum?id=VoLDkQ6yR3 |
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[] | Poster | [] | Cross Attention is a popular method for retrieving information from a set of context tokens for making predictions. At inference time, for each prediction, Cross Attention scans the full set of $\mathcal{O}(N)$ tokens. In practice, however, often only a small subset of tokens are required for good performance. Methods such as Perceiver IO are cheap at inference as they distill the information to a smaller-sized set of latent tokens $L < N$ on which cross attention is then applied, resulting in only $\mathcal{O}(L)$ complexity. However, in practice, as the number of input tokens and the amount of information to distill increases, the number of latent tokens needed also increases significantly. In this work, we propose Tree Cross Attention (TCA) - a module based on Cross Attention that only retrieves information from a logarithmic $\mathcal{O}(\log(N))$ number of tokens for performing inference. TCA organizes the data in a tree structure and performs a tree search at inference time to retrieve the relevant tokens for prediction. Leveraging TCA, we introduce ReTreever, a flexible architecture for token-efficient inference. We show empirically that Tree Cross Attention (TCA) performs comparable to Cross Attention across various classification and uncertainty regression tasks while being significantly more token-efficient. Furthermore, we compare ReTreever against Perceiver IO, showing significant gains while using the same number of tokens for inference. | [] | [] | Tree Cross Attention | [
"Leo Feng",
"Frederick Tung",
"Hossein Hajimirsadeghi",
"Yoshua Bengio",
"Mohamed Osama Ahmed"
] | 2309.17388 | 18,490 | https://openreview.net/forum?id=Vw24wtSddM |
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[] | Poster | [] | Realistic synthetic electronic health records (EHRs) can be leveraged to acceler- ate methodological developments for research purposes while mitigating privacy concerns associated with data sharing. However, the training of Generative Ad- versarial Networks remains challenging, often resulting in issues like mode col- lapse. While diffusion models have demonstrated progress in generating qual- ity synthetic samples for tabular EHRs given ample denoising steps, their perfor- mance wanes when confronted with missing modalities in heterogeneous tabular EHRs data. For example, some EHRs contain solely static measurements, and some contain only contain temporal measurements, or a blend of both data types. To bridge this gap, we introduce FLEXGEN-EHR– a versatile diffusion model tai- lored for heterogeneous tabular EHRs, equipped with the capability of handling missing modalities in an integrative learning framework. We define an optimal transport module to align and accentuate the common feature space of hetero- geneity of EHRs. We empirically show that our model consistently outperforms existing state-of-the-art synthetic EHR generation methods both in fidelity by up to 3.10% and utility by up to 7.16%. Additionally, we show that our method can be successfully used in privacy-sensitive settings, where the original patient-level data cannot be shared. | [] | [] | A Flexible Generative Model for Heterogeneous Tabular EHR with Missing Modality | [
"Huan He",
"William hao",
"Yuanzhe Xi",
"Yong Chen",
"Bradley Malin",
"Joyce Ho"
] | 18,488 | https://openreview.net/forum?id=W2tCmRrj7H |
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[] | Spotlight Poster | [] | Large-scale protein language models (PLMs), such as the ESM family, have achieved remarkable performance in various downstream tasks related to protein structure and function by undergoing unsupervised training on residue sequences. They have become essential tools for researchers and practitioners in biology. However, a limitation of vanilla PLMs is their lack of explicit consideration for protein structure information, which suggests the potential for further improvement. Motivated by this, we introduce the concept of a ``structure-aware vocabulary" that integrates residue tokens with structure tokens. The structure tokens are derived by encoding the 3D structure of proteins using Foldseek. We then propose SaProt, a large-scale general-purpose PLM trained on an extensive dataset comprising approximately 40 million protein sequences and structures. Through extensive evaluation, our SaProt model surpasses well-established and renowned baselines across 10 significant downstream tasks, demonstrating its exceptional capacity and broad applicability. We have made the code, pre-trained model, and all relevant materials available at https://github.com/SaProt/SaProt. | [] | [] | SaProt: Protein Language Modeling with Structure-aware Vocabulary | [
"Jin Su",
"Chenchen Han",
"Yuyang Zhou",
"Junjie Shan",
"Xibin Zhou",
"Fajie Yuan"
] | 19,394 | https://openreview.net/forum?id=6MRm3G4NiU |
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[] | Poster | [] | State-of-the-art systems neuroscience experiments yield large-scale multimodal data, and these data sets require new tools for analysis. Inspired by the success of large pretrained models in vision and language domains, we reframe the analysis of large-scale, cellular-resolution neuronal spiking data into an auto-regressive spatiotemporal generation problem. Neuroformer is a multimodal, multitask generative pre-trained transformer (GPT) model that is specifically designed to handle the intricacies of data in systems neuroscience. It scales linearly with feature size, can process an arbitrary number of modalities, and is adaptable to downstream tasks, such as predicting behavior. We first trained Neuroformer on simulated datasets, and found that it both accurately predicted simulated neuronal circuit activity, and also intrinsically inferred the underlying neural circuit connectivity, including direction. When pretrained to decode neural responses, the model predicted the behavior of a mouse with only few-shot fine-tuning, suggesting that the model begins learning how to do so directly from the neural representations themselves, without any explicit supervision. We used an ablation study to show that joint training on neuronal responses and behavior boosted performance, highlighting the model's ability to associate behavioral and neural representations in an unsupervised manner. These findings show that Neuroformer can analyze neural datasets and their emergent properties, informing the development of models and hypotheses associated with the brain. | [] | [] | Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data | [
"Antonis Antoniades",
"Yiyi Yu",
"Joe S Canzano",
"William Yang Wang",
"Spencer Smith"
] | 2311.00136 | 18,486 | https://openreview.net/forum?id=W8S8SxS9Ng |
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[] | Poster | [] | We study principal-agent problems in which a principal commits to an outcome-dependent payment scheme---called contract---in order to induce an agent to take a costly, unobservable action leading to favorable outcomes. We consider a generalization of the classical (single-round) version of the problem in which the principal interacts with the agent by committing to contracts over multiple rounds. The principal has no information about the agent, and they have to learn an optimal contract by only observing the outcome realized at each round. We focus on settings in which the size of the agent's action space is small. We design an algorithm that learns an approximately-optimal contract with high probability in a number of rounds polynomial in the size of the outcome space, when the number of actions is constant. Our algorithm solves an open problem by Zhu et al. [2022]. Moreover, it can also be employed to provide a $\widetilde{\mathcal{O}}(T^{4/5})$ regret bound in the related online learning setting in which the principal aims at maximizing their cumulative utility, thus considerably improving previously-known regret bounds. | [] | [] | Learning Optimal Contracts: How to Exploit Small Action Spaces | [
"Francesco Bacchiocchi",
"Matteo Castiglioni",
"Alberto Marchesi",
"Nicola Gatti"
] | 2309.09801 | 18,483 | https://openreview.net/forum?id=WKuimaBj4I |
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[] | Poster | [] | Synthetic data (Sim) drawn from simulators have emerged as a popular alternative for training models where acquiring annotated real-world images is difficult. However, transferring models trained on synthetic images to real-world applications can be challenging due to appearance disparities. A commonly employed solution to counter this Sim2Real gap is unsupervised domain adaptation, where models are trained using labeled Sim data and unlabeled Real data. Mispredictions made by such Sim2Real adapted models are often associated with miscalibration – stemming from overconfident predictions on real data. In this paper, we introduce AUGCAL, a simple training-time patch for unsupervised adaptation that improves Sim2Real adapted models by – (1) reducing overall miscalibration, (2) reducing overconfidence in incorrect predictions and (3) improving confidence score reliability by better guiding misclassification detection – all while retaining or improving Sim2Real performance. Given a base Sim2Real adaptation algorithm, at training time, AUGCAL involves replacing vanilla Sim images with strongly augmented views (AUG intervention) and additionally optimizing for a training time calibration loss on augmented Sim predictions (CAL intervention). We motivate AUGCAL using a brief analytical justification of how to reduce miscalibration on unlabeled Real data. Through our experiments, we empirically show the efficacy of AUGCAL across multiple adaptation methods, backbones, tasks and shifts. | [] | [] | AUGCAL: Improving Sim2Real Adaptation by Uncertainty Calibration on Augmented Synthetic Images | [
"Prithvijit Chattopadhyay",
"Bharat Goyal",
"Boglarka Ecsedi",
"Viraj Uday Prabhu",
"Judy Hoffman"
] | 2312.06106 | 18,481 | https://openreview.net/forum?id=WNQjN5HzXt |
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[] | Poster | [
"https://github.com/Docta-ai/docta"
] | Language models have shown promise in various tasks but can be affected by undesired data during training, fine-tuning, or alignment. For example, if some unsafe conversations are wrongly annotated as safe ones, the model fine-tuned on these samples may be harmful. Therefore, the correctness of annotations, i.e., the credibility of the dataset, is important. This study focuses on the credibility of real-world datasets, including the popular benchmarks Jigsaw Civil Comments, Anthropic Harmless & Red Team, PKU BeaverTails & SafeRLHF, that can be used for training a harmless language model. Given the cost and difficulty of cleaning these datasets by humans, we introduce a systematic framework for evaluating the credibility of datasets, identifying label errors, and evaluating the influence of noisy labels in the curated language data, specifically focusing on unsafe comments and conversation classification. With the framework, we find and fix an average of **6.16\%** label errors in **11** datasets constructed from the above benchmarks. The data credibility and downstream learning performance can be remarkably improved by directly fixing label errors, indicating the significance of cleaning existing real-world datasets. | [] | [] | Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models | [
"Zhaowei Zhu",
"Jialu Wang",
"Hao Cheng",
"Yang Liu"
] | 2311.11202 | 19,388 | https://openreview.net/forum?id=6bcAD6g688 |
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[] | Poster | [] | Recent works learn 3D representation explicitly under text-3D guidance. However, limited text-3D data restricts the vocabulary scale and text control of generations. Generators may easily fall into a stereotype concept for certain text prompts, thus losing open-vocabulary generation ability. To tackle this issue, we introduce a conditional 3D generative model, namely TextField3D. Specifically, rather than using the text prompts as input directly, we suggest to inject dynamic noise into the latent space of given text prompts, i.e., Noisy Text Fields (NTFs). In this way, limited 3D data can be mapped to the appropriate range of textual latent space that is expanded by NTFs. To this end, an NTFGen module is proposed to model general text latent code in noisy fields. Meanwhile, an NTFBind module is proposed to align view-invariant image latent code to noisy fields, further supporting image-conditional 3D generation. To guide the conditional generation in both geometry and texture, multi-modal discrimination is constructed with a text-3D discriminator and a text-2.5D discriminator. Compared to previous methods, TextField3D includes three merits: 1) large vocabulary, 2) text consistency, and 3) low latency. Extensive experiments demonstrate that our method achieves a potential open-vocabulary 3D generation capability. | [] | [] | TextField3D: Towards Enhancing Open-Vocabulary 3D Generation with Noisy Text Fields | [
"Tianyu Huang",
"Yihan Zeng",
"Bowen Dong",
"Hang Xu",
"Songcen Xu",
"Rynson W. H. Lau",
"Wangmeng Zuo"
] | 2309.17175 | 18,478 | https://openreview.net/forum?id=WOiOzHG2zD |
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[] | Poster | [] | The fluency and creativity of large pre-trained language models (LLMs) have led to their widespread use, sometimes even as a replacement for traditional search engines. However, language models are prone to making convincing but factually inaccurate claims, often referred to as 'hallucinations', which can harmfully perpetuate myths and misconceptions. Further, manual fact-checking of model responses is a time-consuming process, making human factuality labels expensive to acquire. In this work, we leverage two key recent innovations in NLP to fine-tune language models to be more factual without human labeling, targeting more open-ended generation settings than past work. First, several recent works have proposed methods for scoring the factuality of open-ended text derived from consistency with an external knowledge base or simply a large model's confidence scores. Second, the Direct Preference Optimization algorithm enables straightforward fine-tuning of language models on objectives other than supervised imitation, using a preference ranking over possible model responses. We show that learning from preference rankings generated by either automated criterion significantly improves the factuality of Llama-2 on held-out topics (percent of generated claims that are correct) compared with existing RLHF procedures or decoding strategies targeted at factuality, showing over 50% and 20-30% error reduction for biographies and medical questions respectively. | [] | [] | Fine-Tuning Language Models for Factuality | [
"Katherine Tian",
"Eric Mitchell",
"Huaxiu Yao",
"Christopher D Manning",
"Chelsea Finn"
] | 2311.08401 | 18,477 | https://openreview.net/forum?id=WPZ2yPag4K |
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[] | Poster | [] | Understanding how multiple brain regions interact to produce behavior is a major challenge in systems neuroscience, with many regions causally implicated in common tasks such as sensory processing and decision making. A precise description of interactions between regions remains an open problem. Moreover, neural dynamics are nonlinear and non-stationary. Here, we propose MR-SDS, a multiregion, switching nonlinear state space model that decomposes global dynamics into local and cross-communication components in the latent space. MR-SDS includes directed interactions between brain regions, allowing for estimation of state-dependent communication signals, and accounts for sensory inputs effects, history effects, and heterogeneity across days and animals. We show that our model accurately recovers latent trajectories, vector fields underlying switching nonlinear dynamics, and cross-region communication profiles in three simulations. We then apply our method to two large-scale, multi-region neural datasets involving mouse decision making. The first includes hundreds of neurons per region, recorded simultaneously at single-cell-resolution across 3 distant cortical regions. The second is a mesoscale widefield dataset of 8 adjacent cortical regions imaged across both hemispheres. On these multi-region datasets, our model outperforms existing piece-wise linear multi-region models and reveals multiple distinct dynamical states and a rich set of cross-region communication profiles. | [] | [] | Modeling state-dependent communication between brain regions with switching nonlinear dynamical systems | [
"Orren Karniol-Tambour",
"David M. Zoltowski",
"E. Mika Diamanti",
"Lucas Pinto",
"Carlos D Brody",
"David W. Tank",
"Jonathan W. Pillow"
] | 18,475 | https://openreview.net/forum?id=WQwV7Y8qwa |
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[] | Poster | [
"https://github.com/seunghan96/pits"
] | Masked time series modeling has recently gained much attention as a self-supervised representation learning strategy for time series.Inspired by masked image modeling in computer vision, recent works first patchify and partially mask out time series, and then train Transformers to capture the dependencies between patches by predicting masked patches from unmasked patches.However, we argue that capturing such patch dependencies might not be an optimal strategy for time series representation learning;rather, learning to embed patches independently results in better time series representations.Specifically, we propose to use 1) the simple patch reconstruction task, which autoencode each patch without looking at other patches, and 2) the simple patch-wise MLP that embeds each patch independently.In addition, we introduce complementary contrastive learning to hierarchically capture adjacent time series information efficiently.Our proposed method improves time series forecasting and classification performance compared to state-of-the-art Transformer-based models, while it is more efficient in terms of the number of parameters and training time.The code will be released. | [] | [] | Learning to Embed Time Series Patches Independently | [
"Seunghan Lee",
"Taeyoung Park",
"Kibok Lee"
] | 2312.16427 | 18,474 | https://openreview.net/forum?id=WS7GuBDFa2 |
|
[] | Poster | [] | We present STanHop-Net (Sparse Tandem Hopfield Network) for multivariate time series prediction with memory-enhanced capabilities. At the heart of our approach is STanHop, a novel Hopfield-based neural network block, which sparsely learns and stores both temporal and cross-series representations in a data-dependent fashion. In essence, STanHop sequentially learn temporal representation and cross-series representation using two tandem sparse Hopfield layers. In addition, StanHop incorporates two additional external memory modules: a Plug-and-Play module and a Tune-and-Play module for train-less and task-aware memory-enhancements, respectively. They allow StanHop-Net to fastly respond to certain sudden events. Methodologically, we construct the StanHop-Net by stacking STanHop blocks in a hierarchical fashion, enabling multi-resolution feature extraction with resolution-specific sparsity. Theoretically, we introduce a sparse extension of the modern Hopfield model and show that it endows a tighter memory retrieval error compared to the dense counterpart without sacrificing memory capacity. Empirically, we validate the efficacy of our framework on both synthetic and real-world settings. | [] | [] | STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction | [
"Dennis Wu",
"Jerry Yao-Chieh Hu",
"Weijian Li",
"Bo-Yu Chen",
"Han Liu"
] | 2312.17346 | 19,385 | https://openreview.net/forum?id=6iwg437CZs |
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[] | Poster | [] | In this paper, we investigate the behavior of gradient descent algorithms in physics-informed machine learning methods like PINNs, which minimize residuals connected to partial differential equations (PDEs). Our key result is that the difficulty in training these models is closely related to the conditioning of a specific differential operator. This operator, in turn, is associated to the Hermitian square of the differential operator of the underlying PDE. If this operator is ill-conditioned, it results in slow or infeasible training. Therefore, preconditioning this operator is crucial. We employ both rigorous mathematical analysis and empirical evaluations to investigate various strategies, explaining how they better condition this critical operator, and consequently improve training. | [] | [] | An operator preconditioning perspective on training in physics-informed machine learning | [
"Tim De Ryck",
"Florent Bonnet",
"Siddhartha Mishra",
"Emmanuel de Bezenac"
] | 2310.05801 | 18,473 | https://openreview.net/forum?id=WWlxFtR5sV |
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[] | Poster | [] | We introduce latent intuitive physics, a transfer learning framework for physics simulation that can infer hidden properties of fluids from a single 3D video and simulate the observed fluid in novel scenes. Our key insight is to use latent features drawn from a learnable prior distribution conditioned on the underlying particle states to capture the invisible and complex physical properties. To achieve this, we train a parametrized prior learner given visual observations to approximate the visual posterior of inverse graphics, and both the particle states and the visual posterior are obtained from a learned neural renderer. The converged prior learner is embedded in our probabilistic physics engine, allowing us to perform novel simulations on unseen geometries, boundaries, and dynamics without knowledge of the true physical parameters. We validate our model in three ways: (i) novel scene simulation with the learned visual-world physics, (ii) future prediction of the observed fluid dynamics, and (iii) supervised particle simulation. Our model demonstrates strong performance in all three tasks. | [] | [] | Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Video | [
"Xiangming Zhu",
"Huayu Deng",
"Haochen Yuan",
"Yunbo Wang",
"Xiaokang Yang"
] | 18,472 | https://openreview.net/forum?id=WZu4gUGN13 |
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[] | Poster | [
"https://github.com/bakqui/ST-MEM"
] | Electrocardiograms (ECG) are widely employed as a diagnostic tool for monitoring electrical signals originating from a heart. Recent machine learning research efforts have focused on the application of screening various diseases using ECG signals. However, adapting to the application of screening disease is challenging in that labeled ECG data are limited. Achieving general representation through self-supervised learning (SSL) is a well-known approach to overcome the scarcity of labeled data; however, a naive application of SSL to ECG data, without considering the spatial-temporal relationships inherent in ECG signals, may yield suboptimal results. In this paper, we introduce ST-MEM (Spatio-Temporal Masked Electrocardiogram Modeling), designed to learn spatio-temporal features by reconstructing masked 12-lead ECG data. ST-MEM outperforms other SSL baseline methods in various experimental settings for arrhythmia classification tasks. Moreover, we demonstrate that ST-MEM is adaptable to various lead combinations. Through quantitative and qualitative analysis, we show a spatio-temporal relationship within ECG data. | [] | [] | Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram | [
"Yeongyeon Na",
"Minje Park",
"Yunwon Tae",
"Sunghoon Joo"
] | 2402.09450 | 18,470 | https://openreview.net/forum?id=WcOohbsF4H |
|
[] | Poster | [
"https://github.com/sunset-clouds/WassersteinUniformityMetric"
] | Uniformity plays a crucial role in the assessment of learned representations, contributing to a deeper comprehension of self-supervised learning. The seminal work by \citet{Wang2020UnderstandingCR} introduced a uniformity metric that quantitatively measures the collapse degree of learned representations. Directly optimizing this metric together with alignment proves to be effective in preventing constant collapse. However, we present both theoretical and empirical evidence revealing that this metric lacks sensitivity to dimensional collapse, highlighting its limitations. To address this limitation and design a more effective uniformity metric, this paper identifies five fundamental properties, some of which the existing uniformity metric fails to meet. We subsequently introduce a novel uniformity metric that satisfies all of these desiderata and exhibits sensitivity to dimensional collapse. When applied as an auxiliary loss in various established self-supervised methods, our proposed uniformity metric consistently enhances their performance in downstream tasks. | [] | [] | Rethinking the Uniformity Metric in Self-Supervised Learning | [
"Xianghong Fang",
"Jian Li",
"Qiang Sun",
"Benyou Wang"
] | 2403.00642 | 19,494 | https://openreview.net/forum?id=3pf2hEdu8B |
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[] | Spotlight Poster | [] | Monte Carlo (MC) integration has been employed as the standard approximation method for the Sliced Wasserstein (SW) distance, whose analytical expression involves an intractable expectation. However, MC integration is not optimal in terms of absolute approximation error. To provide a better class of empirical SW, we propose quasi-sliced Wasserstein (QSW) approximations that rely on Quasi-Monte Carlo (QMC) methods. For a comprehensive investigation of QMC for SW, we focus on the 3D setting, specifically computing the SW between probability measures in three dimensions. In greater detail, we empirically evaluate various methods to construct QMC point sets on the 3D unit-hypersphere, including the Gaussian-based and equal area mappings, generalized spiral points, and optimizing discrepancy energies. Furthermore, to obtain an unbiased estimator for stochastic optimization, we extend QSW to Randomized Quasi-Sliced Wasserstein (RQSW) by introducing randomness in the discussed point sets. Theoretically, we prove the asymptotic convergence of QSW and the unbiasedness of RQSW. Finally, we conduct experiments on various 3D tasks, such as point-cloud comparison, point-cloud interpolation, image style transfer, and training deep point-cloud autoencoders, to demonstrate the favorable performance of the proposed QSW and RQSW variants. | [] | [] | Quasi-Monte Carlo for 3D Sliced Wasserstein | [
"Khai Nguyen",
"Nicola Bariletto",
"Nhat Ho"
] | 2309.11713 | 18,469 | https://openreview.net/forum?id=Wd47f7HEXg |
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[] | Poster | [] | Reinforcement Learning with Human Feedback (RLHF) has received significant attention for performing tasks without the need for costly manual reward design by aligning human preferences. It is crucial to consider diverse human feedback types and various learning methods in different environments. However, quantifying progress in RLHF with diverse feedback is challenging due to the lack of standardized annotation platforms and widely used unified benchmarks. To bridge this gap, we introduce **Uni-RLHF**, a comprehensive system implementation tailored for RLHF. It aims to provide a complete workflow from *real human feedback*, fostering progress in the development of practical problems. Uni-RLHF contains three packages: 1) a universal multi-feedback annotation platform, 2) large-scale crowdsourced feedback datasets, and 3) modular offline RLHF baseline implementations. Uni-RLHF develops a user-friendly annotation interface tailored to various feedback types, compatible with a wide range of mainstream RL environments. We then establish a systematic pipeline of crowdsourced annotations, resulting in large-scale annotated datasets comprising more than 15 million steps across 30 popular tasks. Through extensive experiments, the results in the collected datasets demonstrate competitive performance compared to those from well-designed manual rewards. We evaluate various design choices and offer insights into their strengths and potential areas of improvement. We wish to build valuable open-source platforms, datasets, and baselines to facilitate the development of more robust and reliable RLHF solutions based on realistic human feedback. The website is available at https://uni-rlhf.github.io/. | [] | [] | Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedback | [
"Yifu Yuan",
"Jianye HAO",
"Yi Ma",
"Zibin Dong",
"Hebin Liang",
"Jinyi Liu",
"Zhixin Feng",
"Kai Zhao",
"YAN ZHENG"
] | 18,468 | https://openreview.net/forum?id=WesY0H9ghM |
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[] | Poster | [] | Scene graph generation (SGG) models have suffered from inherent problems regarding the benchmark datasets such as the long-tailed predicate distribution and missing annotation problems. In this work, we aim to alleviate the long-tailed problem of SGG by utilizing unannotated triplets. To this end, we introduce a **S**elf-**T**raining framework for **SGG** **(ST-SGG)** that assigns pseudo-labels for unannotated triplets based on which the SGG models are trained. While there has been significant progress in self-training for image recognition, designing a self-training framework for the SGG task is more challenging due to its inherent nature such as the semantic ambiguity and the long-tailed distribution of predicate classes. Hence, we propose a novel pseudo-labeling technique for SGG, called **C**lass-specific **A**daptive **T**hresholding with **M**omentum **(CATM)**, which is a model-agnostic framework that can be applied to any existing SGG models. Furthermore, we devise a graph structure learner (GSL) that is beneficial when adopting our proposed self-training framework to the state-of-the-art message-passing neural network (MPNN)-based SGG models. Our extensive experiments verify the effectiveness of ST-SGG on various SGG models, particularly in enhancing the performance on fine-grained predicate classes. | [] | [] | Adaptive Self-training Framework for Fine-grained Scene Graph Generation | [
"Kibum Kim",
"Kanghoon Yoon",
"Yeonjun In",
"Jinyoung Moon",
"Donghyun Kim",
"Chanyoung Park"
] | 2401.09786 | 18,465 | https://openreview.net/forum?id=WipsLtH77t |
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[] | Poster | [] | Recently years have witnessed a rapid development of large language models (LLMs). Despite the strong ability in many language-understanding tasks, the heavy computational burden largely restricts the application of LLMs especially when one needs to deploy them onto edge devices. In this paper, we propose a quantization-aware low-rank adaptation (QA-LoRA) algorithm. The motivation lies in the imbalanced degrees of freedom of quantization and adaptation, and the solution is to use group-wise operators which increase the degree of freedom of quantization meanwhile decreasing that of adaptation. QA-LoRA is easily implemented with a few lines of code, and it equips the original LoRA with two-fold abilities: (i) during fine-tuning, the LLM's weights are quantized (e.g., into INT4) to reduce time and memory usage; (ii) after fine-tuning, the LLM and auxiliary weights are naturally integrated into a quantized model without loss of accuracy. We apply QA-LoRA to the LLaMA and LLaMA2 model families and validate its effectiveness in different fine-tuning datasets and downstream scenarios. The code is submitted and will be made publically available. | [] | [] | QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models | [
"Yuhui Xu",
"Lingxi Xie",
"Xiaotao Gu",
"Xin Chen",
"Heng Chang",
"Hengheng Zhang",
"Zhengsu Chen",
"XIAOPENG ZHANG",
"Qi Tian"
] | 18,462 | https://openreview.net/forum?id=WvFoJccpo8 |
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[] | Poster | [
"https://github.com/s-kumano/learning-from-adversarial-perturbations"
] | It is not fully understood why adversarial examples can deceive neural networks and transfer between different networks. To elucidate this, several studies have hypothesized that adversarial perturbations, while appearing as noises, contain class features. This is supported by empirical evidence showing that networks trained on mislabeled adversarial examples can still generalize well to correctly labeled test samples. However, a theoretical understanding of how perturbations include class features and contribute to generalization is limited. In this study, we provide a theoretical framework for understanding learning from perturbations using a one-hidden-layer network trained on mutually orthogonal samples. Our results highlight that various adversarial perturbations, even perturbations of a few pixels, contain sufficient class features for generalization. Moreover, we reveal that the decision boundary when learning from perturbations matches that from standard samples except for specific regions under mild conditions. The code is available at https://github.com/s-kumano/learning-from-adversarial-perturbations. | [] | [] | Theoretical Understanding of Learning from Adversarial Perturbations | [
"Soichiro Kumano",
"Hiroshi Kera",
"Toshihiko Yamasaki"
] | 2402.10470 | 18,461 | https://openreview.net/forum?id=Ww9rWUAcdo |
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[] | Poster | [] | Machine learning models have demonstrated substantial performance enhancements over non-learned alternatives in various fundamental data management operations, including indexing (locating items in an array), cardinality estimation (estimating the number of matching records in a database), and range-sum estimation (estimating aggregate attribute values for query-matched records). However, real-world systems frequently favor less efficient non-learned methods due to their ability to offer (worst-case) error guarantees — an aspect where learned approaches often fall short. The primary objective of these guarantees is to ensure system reliability, ensuring that the chosen approach consistently delivers the desired level of accuracy across all databases. In this paper, we embark on the first theoretical study of such guarantees for learned methods, presenting the necessary conditions for such guarantees to hold when using machine learning to perform indexing, cardinality estimation and range-sum estimation. Specifically, we present the first known lower bounds on the model size required to achieve the desired accuracy for these three key database operations. Our results bound the required model size for given average and worst-case errors in performing database operations, serving as the first theoretical guidelines governing how model size must change based on data size to be able to guarantee an accuracy level. More broadly, our established guarantees pave the way for the broader adoption and integration of learned models into real-world systems. | [] | [] | Towards Establishing Guaranteed Error for Learned Database Operations | [
"Sepanta Zeighami",
"Cyrus Shahabi"
] | 19,380 | https://openreview.net/forum?id=6tqgL8VluV |
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[] | Poster | [] | Learning from human preferences is important for language models to match human needs and to align with human and social values. Prior works have achieved remarkable successes by learning from human feedback to understand and follow instructions. Nonetheless, these methods are either founded on hand-picked model generations that are favored by human annotators, rendering them inefficient in terms of data utilization and challenging to apply in general, or they depend on reinforcement learning, which often suffers from imperfect reward functions and relies on extremely challenging optimizations. In this work, we propose a novel technique, Chain of Hindsight, that is easy to optimize and can learn from any form of feedback, regardless of its polarity. Our idea is inspired by how humans learn from extensive feedback presented in the form of languages. We convert all types of feedback into sequences of sentences, which are then used to fine-tune the model, allowing us to take advantage of the language comprehension capabilities of language models.We condition the model on a sequence of model generations paired with feedback. By doing so, the model is trained to generate outputs based on feedback, while learning to identify and correct negative attributes or errors. Applying our method to large language models, we observed that Chain of Hindsight significantly surpasses previous methods in aligning language models with human preferences. We report significant improvements on summarization and dialogue benchmarks, with our approach markedly preferred in human evaluations. | [] | [] | Chain of Hindsight aligns Language Models with Feedback | [
"Hao Liu",
"Carmelo Sferrazza",
"Pieter Abbeel"
] | 2302.02676 | 19,378 | https://openreview.net/forum?id=6xfe4IVcOu |
|
[] | Poster | [
"https://github.com/bethgelab/DataTypeIdentification"
] | Recent advances in the development of vision-language models (VLMs) are yielding remarkable success in recognizing visual semantic content, including impressive instances of compositional image understanding. Here, we introduce the novel task of Visual Data-Type Identification, a basic perceptual skill with implications for data curation (e.g., noisy data-removal from large datasets, domains pecific retrieval) and autonomous vision (e.g., distinguishing changing weather conditions from camera lens staining). We develop two datasets consisting of animal images altered across a diverse set of 27 visual data-types, spanning four broad categories. An extensive zero-shot evaluation of 39 VLMs, ranging from 100M to 80B parameters, shows a nuanced performance landscape. While VLMs are reasonably good at identifying certain stylistic data-types, such as cartoons and sketches, they struggle with simpler data-types arising from basic manipulations like image rotations or additive noise. Our findings reveal that (i) model scaling alone yields marginal gains for contrastively-trained models like CLIP, and (ii) there is a pronounced drop in performance for the largest auto-regressively trained VLMs like OpenFlamingo. This finding points to a blind spot in current frontier VLMs: they excel in recognizing semantic content but fail to acquire anunderstanding of visual data-types through scaling. By analyzing the pre-training distributions of these models and incorporating data-type information into the captions during fine-tuning, we achieve a significant enhancement in performance. By exploring this previously uncharted task, we aim to set the stage for further advancing VLMs to equip them with visual data-type understanding. We will makeour code available online upon publication. | [] | [] | Visual Data-Type Understanding does not emerge from scaling Vision-Language Models | [
"Vishaal Udandarao",
"Max F Burg",
"Samuel Albanie",
"Matthias Bethge"
] | 2310.08577 | 18,460 | https://openreview.net/forum?id=WyEdX2R4er |
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[] | Poster | [] | Searching for novel and diverse molecular candidates is a critical undertaking in drug and material discovery. Existing approaches have successfully adapted the diffusion model, the most effective generative model in image generation, to create 1D SMILES strings, 2D chemical graphs, or 3D molecular conformers. However, these methods are not efficient and flexible enough to generate 3D molecules with multiple desired properties, as they require additional training for the models for each new property or even a new combination of existing properties. Moreover, some properties may potentially conflict, making it impossible to find a molecule that satisfies all of them simultaneously. To address these challenges, we present a training-free conditional 3D molecular generation algorithm based on off-the-shelf unconditional diffusion models and property prediction models. The key techniques include modeling the loss of property prediction models as energy functions, considering the property relation between multiple conditions as a probabilistic graph, and developing a stable posterior estimation for computing the conditional score function. We conducted experiments on both single-objective and multi-objective 3D molecule generation, focusing on quantum properties, and compared our approach with the trained or fine-tuned diffusion models. Our proposed model achieves superior performance in generating molecules that meet the conditions, without any additional training cost. | [] | [] | Training-free Multi-objective Diffusion Model for 3D Molecule Generation | [
"Xu Han",
"Caihua Shan",
"Yifei Shen",
"Can Xu",
"Han Yang",
"Xiang Li",
"Dongsheng Li"
] | 18,459 | https://openreview.net/forum?id=X41c4uB4k0 |
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[] | Poster | [] | Denoising diffusion models enable conditional generation and density modeling of complex relationships like images and text. However, the nature of the learned relationships is opaque making it difficult to understand precisely what relationships between words and parts of an image are captured, or to predict the effect of an intervention. We illuminate the fine-grained relationships learned by diffusion models by noticing a precise relationship between diffusion and information decomposition. Exact expressions for mutual information and conditional mutual information can be written in terms of the denoising model. Furthermore, ${pointwise}$ estimates can be easily estimated as well, allowing us to ask questions about the relationships between specific images and captions. Decomposing information even further to understand which variables in a high-dimensional space carry information is a long-standing problem. For diffusion models, we show that a natural non-negative decomposition of mutual information emerges, allowing us to quantify informative relationships between words and pixels in an image. We exploit these new relations to measure the compositional understanding of diffusion models, to do unsupervised localization of objects in images, and to measure effects when selectively editing images through prompt interventions. | [] | [] | Interpretable Diffusion via Information Decomposition | [
"Xianghao Kong",
"Ollie Liu",
"Han Li",
"Dani Yogatama",
"Greg Ver Steeg"
] | 2310.07972 | 18,458 | https://openreview.net/forum?id=X6tNkN6ate |
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[] | Poster | [] | We propose a new approach for generative modeling based on training a neural network to be idempotent. An idempotent operator is one that can be applied sequentially without changing the result beyond the initial application, namely $f(f(z))=f(z)$. The proposed model $f$ is trained to map a source distribution (e.g, Gaussian noise) to a target distribution (e.g. realistic images) using the following objectives: (1) Instances from the target distribution should map to themselves, namely $f(x)=x$. We define the target manifold as the set of all instances that $f$ maps to themselves.(2) Instances that form the source distribution should map onto the defined target manifold. This is achieved by optimizing the idempotence term, $f(f(z))=f(z)$ which encourages the range of $f(z)$ to be on the target manifold. Under ideal assumptions such a process provably converges to the target distribution. This strategy results in a model capable of generating an output in one step, maintaining a consistent latent space, while also allowing sequential applications for refinement. Additionally, we find that by processing inputs from both target and source distributions, the model adeptly projects corrupted or modified data back to the target manifold. This work is a first step towards a ``global projector'' that enables projecting any input into a target data distribution. | [] | [] | Idempotent Generative Network | [
"Assaf Shocher",
"Amil V Dravid",
"Yossi Gandelsman",
"Inbar Mosseri",
"Michael Rubinstein",
"Alexei A Efros"
] | 2311.01462 | 18,455 | https://openreview.net/forum?id=XIaS66XkNA |
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[] | Poster | [] | With the success of Neural Radiance Field (NeRF) in 3D-aware portrait editing, a variety of works have achieved promising results regarding both quality and 3D consistency. However, these methods heavily rely on per-prompt optimization when handling natural language as editing instructions. Due to the lack of labeled human face 3D datasets and effective architectures, the area of human-instructed 3D-aware editing for open-world portraits in an end-to-end manner remains under-explored. To solve this problem, we propose an end-to-end diffusion-based framework termed $\textbf{InstructPix2NeRF}$, which enables instructed 3D-aware portrait editing from a single open-world image with human instructions. At its core lies a conditional latent 3D diffusion process that lifts 2D editing to 3D space by learning the correlation between the paired images' difference and the instructions via triplet data. With the help of our proposed token position randomization strategy, we could even achieve multi-semantic editing through one single pass with the portrait identity well-preserved. Besides, we further propose an identity consistency module that directly modulates the extracted identity signals into our diffusion process, which increases the multi-view 3D identity consistency. Extensive experiments verify the effectiveness of our method and show its superiority against strong baselines quantitatively and qualitatively. | [] | [] | InstructPix2NeRF: Instructed 3D Portrait Editing from a Single Image | [
"Jianhui Li",
"Shilong Liu",
"Zidong Liu",
"Yikai Wang",
"Kaiwen Zheng",
"Jinghui Xu",
"Jianmin Li",
"Jun Zhu"
] | 2311.02826 | 18,454 | https://openreview.net/forum?id=XIxhINXtQk |
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[] | Poster | [] | One prominent approach toward resolving the adversarial vulnerability of deep neural networks is the two-player zero-sum paradigm of adversarial training, in which predictors are trained against adversarially chosen perturbations of data. Despite the promise of this approach, algorithms based on this paradigm have not engendered sufficient levels of robustness, and suffer from pathological behavior like robust overfitting. To understand this shortcoming, we first show that the commonly used surrogate-based relaxation used in adversarial training algorithms voids all guarantees on the robustness of trained classifiers. The identification of this pitfall informs a novel non-zero-sum bilevel formulation of adversarial training, wherein each player optimizes a different objective function. Our formulation naturally yields a simple algorithmic framework that matches and in some cases outperforms state-of-the-art attacks, attains comparable levels of robustness to standard adversarial training algorithms, and does not suffer from robust overfitting. | [] | [] | Adversarial Training Should Be Cast as a Non-Zero-Sum Game | [
"Alexander Robey",
"Fabian Latorre",
"George J. Pappas",
"Hamed Hassani",
"Volkan Cevher"
] | 2306.11035 | 18,453 | https://openreview.net/forum?id=XJ9vjEAqbx |
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[] | Poster | [] | Conformal prediction has shown spurring performance in constructing statistically rigorous prediction sets for arbitrary black-box machine learning models, assuming the data is exchangeable. However, even small adversarial perturbations during the inference can violate the exchangeability assumption, challenge the coverage guarantees, and result in a subsequent decline in empirical coverage. In this work, we propose a certifiably robust learning-reasoning conformal prediction framework (COLEP) via probabilistic circuits, which comprise a data-driven learning component that trains statistical models to learn different semantic concepts, and a reasoning component that encodes knowledge and characterizes the relationships among the trained models for logic reasoning. To achieve exact and efficient reasoning, we employ probabilistic circuits (PCs) within the reasoning component. Theoretically, we provide end-to-end certification of prediction coverage for COLEP in the presence of $\ell_2$ bounded adversarial perturbations. We also provide certified coverage considering the finite size of the calibration set. Furthermore, we prove that COLEP achieves higher prediction coverage and accuracy over a single model as long as the utilities of knowledge models are non-trivial. Empirically, we show the validity and tightness of our certified coverage, demonstrating the robust conformal prediction of COLEP on various datasets, including GTSRB, CIFAR10, and AwA2. We show that COLEP achieves up to 12% improvement in certified coverage on GTSRB, 9% on CIFAR-10, and 14% on AwA2. | [] | [] | COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits | [
"Mintong Kang",
"Nezihe Merve Gürel",
"Linyi Li",
"Bo Li"
] | 2403.11348 | 18,452 | https://openreview.net/forum?id=XN6ZPINdSg |
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[] | Poster | [] | An extension of Transformers is proposed that enables explicit relational reasoning through a novel module called the *Abstractor*. At the core of the Abstractor is a variant of attention called *relational cross-attention*. The approach is motivated by an architectural inductive bias for relational learning that disentangles relational information from extraneous features about individual objects. This enables explicit relational reasoning, supporting abstraction and generalization from limited data. The Abstractor is first evaluated on simple discriminative relational tasks and compared to existing relational architectures. Next, the Abstractor is evaluated on purely relational sequence-to-sequence tasks, where dramatic improvements are seen in sample efficiency compared to standard Transformers. Finally, Abstractors are evaluated on a collection of tasks based on mathematical problem solving, where modest but consistent improvements in performance and sample efficiency are observed. | [] | [] | Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers | [
"Awni Altabaa",
"Taylor Whittington Webb",
"Jonathan D. Cohen",
"John Lafferty"
] | 18,451 | https://openreview.net/forum?id=XNa6r6ZjoB |
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[] | Poster | [] | We consider the optimization of complex performance metrics in multi-label classification under the population utility framework. We mainly focus on metrics linearly decomposable into a sum of binary classification utilities applied separately to each label with an additional requirement of exactly $k$ labels predicted for each instance. These ``macro-at-$k$'' metrics possess desired properties for extreme classification problems with long tail labels. Unfortunately, the at-$k$ constraint couples the otherwise independent binary classification tasks, leading to a much more challenging optimization problem than standard macro-averages. We provide a statistical framework to study this problem, prove the existence and the form of the optimal classifier, and propose a statistically consistent and practical learning algorithm based on the Frank-Wolfe method. Interestingly, our main results concern even more general metrics being non-linear functions of label-wise confusion matrices. Empirical results provide evidence for the competitive performance of the proposed approach. | [] | [] | Consistent algorithms for multi-label classification with macro-at-$k$ metrics | [
"Erik Schultheis",
"Wojciech Kotlowski",
"Marek Wydmuch",
"Rohit Babbar",
"Strom Borman",
"Krzysztof Dembczynski"
] | 18,450 | https://openreview.net/forum?id=XOnya9gSdF |
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[] | Poster | [] | Exemplar-Free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper, we consider the challenging Cold Start scenario in which insufficient data is available in the first task to learn a high-quality backbone. This is especially challenging for EFCIL since it requires high plasticity, which results in feature drift which is difficult to compensate for in the exemplar-free setting. To address this problem, we propose a simple and effective approach that consolidates feature representations by regularizing drift in directions highly relevant to previous tasks and employs prototypes to reduce task-recency bias. Our method, called Elastic Feature Consolidation (EFC), exploits a tractable second-order approximation of feature drift based on an Empirical Feature Matrix (EFM). The EFM induces a pseudo-metric in feature space which we use to regularize feature drift in important directions and to update Gaussian prototypes used in a novel asymmetric cross entropy loss which effectively balances prototype rehearsal with data from new tasks. Experimental results on CIFAR-100, Tiny-ImageNet, ImageNet-Subset and ImageNet-1K demonstrate that Elastic Feature Consolidation is better able to learn new tasks by maintaining model plasticity and significantly outperform the state-of-the-art. | [] | [] | Elastic Feature Consolidation For Cold Start Exemplar-Free Incremental Learning | [
"Simone Magistri",
"Tomaso Trinci",
"Albin Soutif",
"Joost van de Weijer",
"Andrew D. Bagdanov"
] | 2402.03917 | 19,370 | https://openreview.net/forum?id=7D9X2cFnt1 |
|
[
"context-mt/scat-marian-small-ctx4-cwd1-en-fr",
"context-mt/scat-marian-small-target-ctx4-cwd0-en-fr",
"context-mt/scat-marian-big-ctx4-cwd1-en-fr",
"context-mt/scat-marian-big-target-ctx4-cwd0-en-fr",
"context-mt/scat-mbart50-1toM-ctx4-cwd1-en-fr",
"context-mt/scat-mbart50-1toM-target-ctx4-cwd0-en-fr"
] | Poster | [
"https://github.com/gsarti/pecore"
] | Establishing whether language models can use contextual information in a human-plausible way is important to ensure their safe adoption in real-world settings. However, the questions of $\textit{when}$ and $\textit{which parts}$ of the context affect model generations are typically tackled separately, and current plausibility evaluations are practically limited to a handful of artificial benchmarks. To address this, we introduce $\textbf{P}$lausibility $\textbf{E}$valuation of $\textbf{Co}$ntext $\textbf{Re}$liance (PECoRe), an end-to-end interpretability framework designed to quantify context usage in language models' generations. Our approach leverages model internals to (i) contrastively identify context-sensitive target tokens in generated texts and (ii) link them to contextual cues justifying their prediction. We use PECoRe to quantify the plausibility of context-aware machine translation models, comparing model rationales with human annotations across several discourse-level phenomena. Finally, we apply our method to unannotated generations to identify context-mediated predictions and highlight instances of (im)plausible context usage in model translations. | [
"gsarti/pecore"
] | [
"gsarti/iwslt2017_context",
"inseq/scat",
"inseq/disc_eval_mt"
] | Quantifying the Plausibility of Context Reliance in Neural Machine Translation | [
"Gabriele Sarti",
"Grzegorz Chrupała",
"Malvina Nissim",
"Arianna Bisazza"
] | 2310.01188 | 18,449 | https://openreview.net/forum?id=XTHfNGI3zT |
|
[] | Poster | [
"https://github.com/Westlake-AI/MogaNet"
] | By contextualizing the kernel as globally as possible, Modern ConvNets have shown great potential in computer vision tasks. However, recent progress of \textit{multi-order game-theoretic interaction} in deep neural networks (DNNs) shows that the representation capacity of modern ConvNets has not been well unleashed, where the most expressive interactions have not been effectively encoded with the increased kernel size. To address this challenge, we propose a new family of modern ConvNets, dubbed MogaNet, for discriminative visual representation learning in pure ConvNet-based models, with preferable complexity-performance trade-offs. MogaNet encapsulates conceptually simple yet effective convolutions and gated aggregation into a compact module, where discriminative features are efficiently gathered and contextualized in an adaptive manner. Extensive experiments show that MogaNet exhibits great scalability, impressive efficiency of model parameters, and competitive performance compared to state-of-the-art ViTs and ConvNets on ImageNet and various downstream vision benchmarks, including COCO object detection, ADE20K semantic segmentation, 2D\&3D human pose estimation, and video prediction. Notably, MogaNet hits 80.0\% and 87.8\% accuracy with 5.2M and 181M parameters on ImageNet-1K, outperforming ParC-Net and ConvNeXt-L, while saving 59\% FLOPs and 17M parameters, respectively. | [] | [] | MogaNet: Multi-order Gated Aggregation Network | [
"Siyuan Li",
"Zedong Wang",
"Zicheng Liu",
"Cheng Tan",
"Haitao Lin",
"Di Wu",
"Zhiyuan Chen",
"Jiangbin Zheng",
"Stan Z. Li"
] | 2211.03295 | 18,447 | https://openreview.net/forum?id=XhYWgjqCrV |
|
[] | Spotlight Poster | [] | Robotic systems that rely primarily on self-supervised learning have the potential to decrease the amount of human annotation and engineering effort required to learn control strategies. In the same way that prior robotic systems have leveraged self-supervised techniques from computer vision (CV) and natural language processing (NLP), our work builds on prior work showing that the reinforcement learning (RL) itself can be cast as a self-supervised problem: learning to reach any goal without human-specified rewards or labels. Despite the seeming appeal, little (if any) prior work has demonstrated how self-supervised RL methods can be practically deployed on robotic systems. By first studying a challenging simulated version of this task, we discover design decisions about architectures and hyperparameters that increase the success rate by $2 \times$. These findings lay the groundwork for our main result: we demonstrate that a self-supervised RL algorithm based on contrastive learning can solve real-world, image-based robotic manipulation tasks, with tasks being specified by a single goal image provided after training. | [] | [] | Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data | [
"Chongyi Zheng",
"Benjamin Eysenbach",
"Homer Rich Walke",
"Patrick Yin",
"Kuan Fang",
"Ruslan Salakhutdinov",
"Sergey Levine"
] | 2306.03346 | 18,446 | https://openreview.net/forum?id=Xkf2EBj4w3 |
|
[] | Poster | [] | Training machine learning models with differential privacy (DP) has received increasing interest in recent years. One of the most popular algorithms for training differentially private models is differentially private stochastic gradient descent (DPSGD) and its variants, where at each step gradients are clipped and combined with some noise. Given the increasing usage of DPSGD, we ask the question: is DPSGD alone sufficient to find a good minimizer for every dataset under privacy constraints? As a first step towards answering this question, we show that even for the simple case of linear classification, unlike non-private optimization, (private) feature preprocessing is vital for differentially private optimization. In detail, we first show theoretically that there exists an example where without feature preprocessing, DPSGD incurs a privacy error proportional to the maximum norm of features over all samples. We then propose an algorithm called *DPSGD-F*, which combines DPSGD with feature preprocessing and prove that for classification tasks, it incurs a privacy error proportional to the diameter of the features $\max_{x, x' \in D} \|x - x'\|_2$. We then demonstrate the practicality of our algorithm on image classification benchmarks. | [] | [] | The importance of feature preprocessing for differentially private linear optimization | [
"Ziteng Sun",
"Ananda Theertha Suresh",
"Aditya Krishna Menon"
] | 2307.11106 | 18,445 | https://openreview.net/forum?id=XlTDBZFXWp |
|
[] | Poster | [] | Large language models (LLMs) have notably enhanced the fluency and diversity of machine-generated text. However, this progress also presents a significant challenge in detecting the origin of a given text, and current research on detection methods lags behind the rapid evolution of LLMs. Conventional training-based methods have limitations in flexibility, particularly when adapting to new domains, and they often lack explanatory power. To address this gap, we propose a novel training-free detection strategy called Divergent N-Gram Analysis (DNA-GPT). Given a text, we first truncate it in the middle and then use only the preceding portion as input to the LLMs to regenerate the new remaining parts. By analyzing the differences between the original and new remaining parts through N-gram analysis in black-box or probability divergence in white-box, we can clearly illustrate significant discrepancies between machine-generated and human-written text. We conducted extensive experiments on the most advanced LLMs from OpenAI, including text-davinci-003, GPT-3.5-turbo, and GPT-4, as well as open-source models such as GPT-NeoX-20B and LLaMa-13B. Results show that our zero-shot approach exhibits state-of-the-art performance in distinguishing between human and GPT-generated text on four English and one German dataset, outperforming OpenAI's own classifier, which is trained on millions of text. Additionally, our methods provide reasonable explanations and evidence to support our claim, which is a unique feature of explainable detection. Our method is also robust under the revised text attack and can additionally solve model sourcing. | [] | [] | DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text | [
"Xianjun Yang",
"Wei Cheng",
"Yue Wu",
"Linda Ruth Petzold",
"William Yang Wang",
"Haifeng Chen"
] | 18,444 | https://openreview.net/forum?id=Xlayxj2fWp |
||
[] | Spotlight Poster | [] | Diffusion models have emerged as a powerful method of generative modeling across a range of fields, capable of producing stunning photo-realistic images from natural language descriptions. However, these models lack explicit control over the 3D structure in the generated images. Consequently, this hinders our ability to obtain detailed 3D annotations for the generated images or to craft instances with specific poses and distances. In this paper, we propose a simple yet effective method that incorporates 3D geometry control into diffusion models. Our method exploits ControlNet, which extends diffusion models by using visual prompts in addition to text prompts. We generate images of the 3D objects taken from 3D shape repositories (e.g., ShapeNet and Objaverse), render them from a variety of poses and viewing directions, compute the edge maps of the rendered images, and use these edge maps as visual prompts to generate realistic images. With explicit 3D geometry control, we can easily change the 3D structures of the objects in the generated images and obtain ground-truth 3D annotations automatically. This allows us to improve a wide range of vision tasks, e.g., classification and 3D pose estimation, in both in-distribution (ID) and out-of-distribution (OOD) settings. We demonstrate the effectiveness of our method through extensive experiments on ImageNet-100, ImageNet-R, PASCAL3D+, ObjectNet3D, and OOD-CV. The results show that our method significantly outperforms existing methods across multiple benchmarks, e.g., 3.8 percentage points on ImageNet-100 using DeiT-B and 3.5 percentage points on PASCAL3D+ & ObjectNet3D using NeMo. | [] | [] | Generating Images with 3D Annotations Using Diffusion Models | [
"Wufei Ma",
"Qihao Liu",
"Jiahao Wang",
"Angtian Wang",
"Xiaoding Yuan",
"Yi Zhang",
"Zihao Xiao",
"Guofeng Zhang",
"Beijia Lu",
"Ruxiao Duan",
"Yongrui Qi",
"Adam Kortylewski",
"Yaoyao Liu",
"Alan Yuille"
] | 2306.08103 | 18,443 | https://openreview.net/forum?id=XlkN11Xj6J |
|
[] | Poster | [] | Recent work by Power et al. (2022) highlighted a surprising "grokking" phenomenon in learning arithmetic tasks: a neural net first "memorizes" the training set, resulting in perfect training accuracy but near-random test accuracy, and after training for sufficiently longer, it suddenly transitions to perfect test accuracy. This paper studies the grokking phenomenon in theoretical setups and shows that it can be induced by a dichotomy of early and late phase implicit biases. Specifically, when training homogeneous neural nets with large initialization and small weight decay on both classification and regression tasks, we prove that the training process gets trapped at a solution corresponding to a kernel predictor for a long time, and then a very sharp transition to min-norm/max-margin predictors occurs, leading to a dramatic change in test accuracy. Even in the absence of weight decay, we show that grokking can still happen when the late phase implicit bias is driven by other regularization mechanisms, such as implicit margin maximization or sharpness reduction. | [] | [] | Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking | [
"Kaifeng Lyu",
"Jikai Jin",
"Zhiyuan Li",
"Simon Shaolei Du",
"Jason D. Lee",
"Wei Hu"
] | 2311.18817 | 18,441 | https://openreview.net/forum?id=XsHqr9dEGH |
|
[] | Poster | [] | We propose and study a realistic Continual Learning (CL) setting where learning algorithms are granted a restricted computational budget per time step while training. We apply this setting to large-scale semi-supervised Continual Learning scenarios with sparse label rate. Previous proficient CL methods perform very poorly in this challenging setting. Overfitting to the sparse labeled data and insufficient computational budget are the two main culprits for such a poor performance. Our new setting encourages learning methods to effectively and efficiently utilize the unlabeled data during training. To that end, we propose a simple but highly effective baseline, DietCL, which utilizes both unlabeled and labeled data jointly. DietCL meticulously allocates computational budget for both types of data. We validate our baseline, at scale, on several datasets, e.g., CLOC, ImageNet10K, and CGLM, under constraint budget setup. DietCL outperforms, by a large margin, all existing supervised CL algorithms as well as more recent continual semi-supervised methods. Our extensive analysis and ablations demonstrate that DietCL is stable under a full spectrum of label sparsity, computational budget and various other ablations. | [] | [] | Continual Learning on a Diet: Learning from Sparsely Labeled Streams Under Constrained Computation | [
"Wenxuan Zhang",
"Youssef Mohamed",
"Bernard Ghanem",
"Philip Torr",
"Adel Bibi",
"Mohamed Elhoseiny"
] | 18,440 | https://openreview.net/forum?id=Xvfz8NHmCj |
||
[] | Poster | [] | Calibration measures and reliability diagrams are two fundamental tools for measuring and interpreting the calibration of probabilistic predictors. Calibration measures quantify the degree of miscalibration, and reliability diagrams visualize the structure of this miscalibration. However, the most common constructions of reliability diagrams and calibration measures --- binning and ECE --- both suffer from well-known flaws (e.g. discontinuity). We show that a simple modification fixes both constructions: first smooth the observations using an RBF kernel, then compute the Expected Calibration Error (ECE) of this smoothed function. We prove that with a careful choice of bandwidth, this method yields a calibration measure that is well-behaved in the sense of (Blasiok, Gopalan, Hu, and Nakkiran 2023) --- a consistent calibration measure. We call this measure the SmoothECE. Moreover, the reliability diagram obtained from this smoothed function visually encodes the SmoothECE, just as binned reliability diagrams encode the BinnedECE. We also provide a Python package with simple, hyperparameter-free methods for measuring and plotting calibration (not linked to preserve anonymity). | [] | [] | Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing | [
"Jaroslaw Blasiok",
"Preetum Nakkiran"
] | 2309.12236 | 18,439 | https://openreview.net/forum?id=XwiA1nDahv |
|
[] | Poster | [] | Recently, 3D generative models have shown promising performances in structure-based drug design by learning to generate ligands given target binding sites. However, only modeling the target-ligand distribution can hardly fulfill one of the main goals in drug discovery -- designing novel ligands with desired properties, e.g., high binding affinity, easily synthesizable, etc. This challenge becomes particularly pronounced when the target-ligand pairs used for training do not align with these desired properties. Moreover, most existing methods aim at solving de novo design task, while many generative scenarios requiring flexible controllability, such as R-group optimization and scaffold hopping, have received little attention. In this work, we propose DecompOpt, a structure-based molecular optimization method based on a controllable and decomposed diffusion model. DecompOpt presents a new generation paradigm which combines optimization with conditional diffusion models to achieve desired properties while adhering to the molecular grammar. Additionally, DecompOpt offers a unified framework covering both de novo design and controllable generation. To achieve so, ligands are decomposed into substructures which allows fine-grained control and local optimization. Experiments show that DecompOpt can efficiently generate molecules with improved properties than strong de novo baselines, and demonstrate great potential in controllable generation tasks. | [] | [] | DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization | [
"Xiangxin Zhou",
"Xiwei Cheng",
"Yuwei Yang",
"Yu Bao",
"Liang Wang",
"Quanquan Gu"
] | 2403.13829 | 18,436 | https://openreview.net/forum?id=Y3BbxvAQS9 |
|
[] | Poster | [] | Large Language Models (LLMs) combined with instruction tuning have made significant progress when generalizing to unseen tasks. However, they have been less successful in Information Extraction (IE), lagging behind task-specific models. Typically, IE tasks are characterized by complex annotation guidelines which describe the task and give examples to humans. Previous attempts to leverage such information have failed, even with the largest models, as they are not able to follow the guidelines out-of-the-box. In this paper we propose GoLLIE (Guideline-following Large Language Model for IE), a model able to improve zero-shot results on unseen IE tasks by virtue of being fine-tuned to comply with annotation guidelines. Comprehensive evaluation empirically demonstrates that GoLLIE is able to generalize to and follow unseen guidelines, outperforming previous attempts at zero-shot information extraction. The ablation study shows that detailed guidelines is key for good results. Code, data and models will be made publicly available. | [] | [] | GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction | [
"Oscar Sainz",
"Iker García-Ferrero",
"Rodrigo Agerri",
"Oier Lopez de Lacalle",
"German Rigau",
"Eneko Agirre"
] | 2310.03668 | 18,435 | https://openreview.net/forum?id=Y3wpuxd7u9 |
|
[] | Spotlight Poster | [] | One-shot 3D talking portrait generation aims to reconstruct a 3D avatar from an unseen image, and then animate it with a reference video or audio to generate a talking portrait video. The existing methods fail to simultaneously achieve the goals of accurate 3D avatar reconstruction and stable talking face animation. Besides, while the existing works mainly focus on synthesizing the head part, it is also vital to generate natural torso and background segments to obtain a realistic talking portrait video. To address these limitations, we present Real3D-Potrait, a framework that (1) improves the one-shot 3D reconstruction power with a large image-to-plane model that distills 3D prior knowledge from a 3D face generative model; (2) facilitates accurate motion-conditioned animation with an efficient motion adapter; (3) synthesizes realistic video with natural torso movement and switchable background using a head-torso-background super-resolution model; and (4) supports one-shot audio-driven talking face generation with a generalizable audio-to-motion model. Extensive experiments show that Real3D-Portrait generalizes well to unseen identities and generates more realistic talking portrait videos compared to previous methods. Video samples are available at https://real3dportrait.github.io. | [] | [] | Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis | [
"Zhenhui Ye",
"Tianyun Zhong",
"Yi Ren",
"Jiaqi Yang",
"Weichuang Li",
"Jiawei Huang",
"Ziyue Jiang",
"Jinzheng He",
"Rongjie Huang",
"Jinglin Liu",
"Chen Zhang",
"Xiang Yin",
"Zejun MA",
"Zhou Zhao"
] | 2401.08503 | 19,369 | https://openreview.net/forum?id=7ERQPyR2eb |
|
[] | Poster | [] | The aligned Large Language Models (LLMs) are powerful language understanding and decision-making tools that are created through extensive alignment with human feedback. However, these large models remain susceptible to jailbreak attacks, where adversaries manipulate prompts to elicit malicious outputs that should not be given by aligned LLMs. Investigating jailbreak prompts can lead us to delve into the limitations of LLMs and further guide us to secure them. Unfortunately, existing jailbreak techniques suffer from either (1) scalability issues, where attacks heavily rely on manual crafting of prompts, or (2) stealthiness problems, as attacks depend on token-based algorithms to generate prompts that are often semantically meaningless, making them susceptible to detection through basic perplexity testing. In light of these challenges, we intend to answer this question: Can we develop an approach that can automatically generate stealthy jailbreak prompts? In this paper, we introduce AutoDAN, a novel jailbreak attack against aligned LLMs. AutoDAN can automatically generate stealthy jailbreak prompts by the carefully designed hierarchical genetic algorithm. Extensive evaluations demonstrate that AutoDAN not only automates the process while preserving semantic meaningfulness, but also demonstrates superior attack strength in cross-model transferability, and cross-sample universality compared with the baseline. Moreover, we also compare AutoDAN with perplexity-based defense methods and show that AutoDAN can bypass them effectively. | [] | [] | AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models | [
"Xiaogeng Liu",
"Nan Xu",
"Muhao Chen",
"Chaowei Xiao"
] | 2310.04451 | 19,366 | https://openreview.net/forum?id=7Jwpw4qKkb |
|
[] | Poster | [] | Given the ever-increasing size of modern neural networks, the significance of sparse architectures has surged due to their accelerated inference speeds and minimal memory demands. When it comes to global pruning techniques, Iterative Magnitude Pruning (IMP) still stands as a state-of-the-art algorithm despite its simple nature, particularly in extremely sparse regimes. In light of the recent finding that the two successive matching IMP solutions are linearly connected without a loss barrier, we propose Sparse Weight Averaging with Multiple Particles (SWAMP), a straightforward modification of IMP that achieves performance comparable to an ensemble of two IMP solutions. For every iteration, we concurrently train multiple sparse models, referred to as particles, using different batch orders yet the same matching ticket, and then weight average such models to produce a single mask. We demonstrate that our method consistently outperforms existing baselines across different sparsities through extensive experiments on various neural network structures and data. | [] | [] | Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning | [
"Moonseok Choi",
"Hyungi Lee",
"Giung Nam",
"Juho Lee"
] | 2305.14852 | 18,434 | https://openreview.net/forum?id=Y9t7MqZtCR |
|
[] | Poster | [] | Large foundation models are becoming ubiquitous, but training them from scratch is prohibitively expensive. Thus, efficiently adapting these powerful models to downstream tasks is increasingly important. In this paper, we study a principled finetuning paradigm -- Orthogonal Finetuning (OFT) -- for downstream task adaptation. Despite demonstrating good generalizability, OFT still uses a fairly large number of trainable parameters due to the high dimensionality of orthogonal matrices. To address this, we start by examining OFT from an information transmission perspective, and then identify a few key desiderata that enable better parameter-efficiency. Inspired by how the Cooley-Tukey fast Fourier transform algorithm enables efficient information transmission, we propose an efficient orthogonal parameterization using butterfly structures. We apply this parameterization to OFT, creating a novel parameter-efficient finetuning method, called Orthogonal Butterfly (BOFT). By subsuming OFT as a special case, BOFT introduces a generalized orthogonal finetuning framework. Finally, we conduct an extensive empirical study of adapting large vision transformers, large language models, and text-to-image diffusion models to various downstream tasks in computer vision and natural language. The results validate the effectiveness of BOFT as a generic finetuning method. | [] | [] | Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization | [
"Weiyang Liu",
"Zeju Qiu",
"Yao Feng",
"Yuliang Xiu",
"Yuxuan Xue",
"Longhui Yu",
"Haiwen Feng",
"Zhen Liu",
"Juyeon Heo",
"Songyou Peng",
"Yandong Wen",
"Michael J. Black",
"Adrian Weller",
"Bernhard Schölkopf"
] | 2311.06243 | 19,363 | https://openreview.net/forum?id=7NzgkEdGyr |
|
[] | Poster | [] | Human feedback has become the de facto standard for evaluating the performance of Large Language Models, and is increasingly being used as a training objective. However, it is not clear which properties of a generated output this single `preference' score captures. We hypothesise that preference scores are subjective and open to undesirable biases. We critically analyse the use of human feedback for both training and evaluation, to verify whether it fully captures a range of crucial error criteria. We find that while preference scores have fairly good coverage, they under-represent important aspects like factuality. We further hypothesise that both preference scores and error annotation may be affected by confounders, and leverage instruction-tuned models to generate outputs that vary along two possible confounding dimensions: assertiveness and complexity. We find that the assertiveness of an output skews the perceived rate of factuality errors, indicating that human annotations are not a fully reliable evaluation metric or training objective. Finally, we offer preliminary evidence that using human feedback as a training objective disproportionately increases the assertiveness of model outputs. We encourage future work to carefully consider whether preference scores are well aligned with the desired objective. | [] | [] | Human Feedback is not Gold Standard | [
"Tom Hosking",
"Phil Blunsom",
"Max Bartolo"
] | 2309.16349 | 19,356 | https://openreview.net/forum?id=7W3GLNImfS |
|
[] | Poster | [] | This paper presents a Domain-Inspired Sharpness Aware Minimization (DISAM) algorithm for optimization under domain shifts. It is motivated by the inconsistent convergence degree of SAM across different domains, which induces optimization bias towards certain domains and thus impairs the overall convergence. To address this issue, we consider the domain-level convergence consistency in the sharpness estimation to prevent the overwhelming (deficient) perturbations for less (well) optimized domains. Specifically, DISAM introduces the constraint of minimizing variance in the domain loss, which allows the elastic gradient calibration in perturbation generation: when one domain is optimized above the averaging level \textit{w.r.t.} loss, the gradient perturbation towards that domain will be weakened automatically, and vice versa. Under this mechanism, we theoretically show that DISAM can achieve faster overall convergence and improved generalization in principle when inconsistent convergence emerges. Extensive experiments on various domain generalization benchmarks show the superiority of DISAM over a range of state-of-the-art methods. Furthermore, we show the superior efficiency of DISAM in parameter-efficient fine-tuning combined with the pretraining models. | [] | [] | Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts | [
"Ruipeng Zhang",
"Ziqing Fan",
"Jiangchao Yao",
"Ya Zhang",
"Yanfeng Wang"
] | 18,973 | https://openreview.net/forum?id=I4wB3HA3dJ |
||
[] | Poster | [] | Image outpainting aims to generate the content of an input sub-image beyond its original boundaries. It is an important task in content generation yet remains an open problem for generative models. This paper pushes the technical frontier of image outpainting in two directions that have not been resolved in literature: 1) outpainting with arbitrary and continuous multiples (without restriction), and 2) outpainting in a single step (even for large expansion multiples). Moreover, we develop a method that does not depend on a pre-trained backbone network, which is in contrast commonly required by the previous SOTA outpainting methods. The arbitrary multiple outpainting is achieved by utilizing randomly cropped views from the same image during training to capture arbitrary relative positional information. Specifically, by feeding one view and positional embeddings as queries, we can reconstruct another view. At inference, we generate images with arbitrary expansion multiples by inputting an anchor image and its corresponding positional embeddings. The one-step outpainting ability here is particularly noteworthy in contrast to previous methods that need to be performed for $N$ times to obtain a final multiple which is $N$ times of its basic and fixed multiple. We evaluate the proposed approach (called PQDiff as we adopt a diffusion-based generator as our embodiment, under our proposed \textbf{P}ositional \textbf{Q}uery scheme) on public benchmarks, demonstrating its superior performance over state-of-the-art approaches. Specifically, PQDiff achieves state-of-the-art FID scores on the Scenery (\textbf{21.512}), Building Facades (\textbf{25.310}), and WikiArts (\textbf{36.212}) datasets. Furthermore, under the 2.25x, 5x and 11.7x outpainting settings, PQDiff only takes \textbf{40.6\%}, \textbf{20.3\%} and \textbf{10.2\%} of the time of the benchmark state-of-the-art (SOTA) method. | [] | [] | Continuous-Multiple Image Outpainting in One-Step via Positional Query and A Diffusion-based Approach | [
"Shaofeng Zhang",
"Jinfa Huang",
"Qiang Zhou",
"zhibin wang",
"Fan Wang",
"Jiebo Luo",
"Junchi Yan"
] | 2401.15652 | 19,348 | https://openreview.net/forum?id=7hxoYxKDTV |
|
[] | Poster | [] | Designing expressive Graph Neural Networks (GNNs) is an important topic in graph machine learning fields. Despite the existence of numerous approaches proposed to enhance GNNs based on Weisfeiler-Lehman (WL) tests, what GNNs \emph{can and cannot} learn still lacks a deeper understanding. This paper adopts a fundamentally different approach to examine the expressive power of GNNs from a probabilistic perspective. By establishing connections between GNNs' predictions and the central inference problems of probabilistic graphical models (PGMs), we can analyze previous GNN variants with a novel hierarchical framework and gain new insights into their node-level and link-level behaviors. Additionally, we introduce novel methods that can provably enhance GNNs' ability to capture complex dependencies and make complex predictions. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of our approaches. | [] | [] | Rethinking and Extending the Probabilistic Inference Capacity of GNNs | [
"Tuo Xu",
"Lei Zou"
] | 19,345 | https://openreview.net/forum?id=7vVWiCrFnd |
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[] | Spotlight Poster | [] | Optimization layers within neural network architectures have become increasingly popular for their ability to solve a wide range of machine learning tasks and to model domain-specific knowledge. However, designing optimization layers requires careful consideration as the underlying optimization problems might be infeasible during training. Motivated by applications in learning, control and robotics, this work focuses on convex quadratic programming (QP) layers. The specific structure of this type of optimization layer can be efficiently exploited for faster computations while still allowing rich modeling capabilities. We leverage primal-dual augmented Lagrangian techniques for computing derivatives of both feasible and infeasible QP solutions. More precisely, we propose a unified approach which tackles the differentiability of the closest feasible QP solutions in a classical $\ell_2$ sense. The obtained Jacobian covers for feasible QPs the traditional implicit differentiation when it is valid and a weaker notion (i.e., conservative Jacobian) when it is infeasible. We then harness this approach to enrich the expressive capabilities of existing QP layers. More precisely, we show how differentiating through infeasible QPs during training enables to drive towards feasibility at test time a new range of QP layers. These layers notably demonstrate superior predictive performance in some conventional learning tasks. Additionally, we present alternative formulations that enhance numerical robustness, speed, and accuracy for training such layers. Along with these contributions, we provide an open-source C++ software package called QPLayer for differentiating feasible and infeasible convex QPs and which can be interfaced with modern learning frameworks. | [] | [] | Leveraging augmented-Lagrangian techniques for differentiating over infeasible quadratic programs in machine learning | [
"Antoine Bambade",
"Fabian Schramm",
"Adrien Taylor",
"Justin Carpentier"
] | 18,433 | https://openreview.net/forum?id=YCPDFfmkFr |
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[] | Poster | [] | Diffusion models are a class of flexible generative models trained with an approximation to the log-likelihood objective. However, most use cases of diffusion models are not concerned with likelihoods, but instead with downstream objectives such as human-perceived image quality or drug effectiveness. In this paper, we investigate reinforcement learning methods for directly optimizing diffusion models for such objectives. We describe how posing denoising as a multi-step decision-making problem enables a class of policy gradient algorithms, which we refer to as denoising diffusion policy optimization (DDPO), that are more effective than alternative reward-weighted likelihood approaches. Empirically, DDPO is able to adapt text-to-image diffusion models to objectives that are difficult to express via prompting, such as image compressibility, and those derived from human feedback, such as aesthetic quality. Finally, we show that DDPO can improve prompt-image alignment using feedback from a vision-language model without the need for additional data collection or human annotation. | [] | [] | Training Diffusion Models with Reinforcement Learning | [
"Kevin Black",
"Michael Janner",
"Yilun Du",
"Ilya Kostrikov",
"Sergey Levine"
] | 2305.13301 | 18,432 | https://openreview.net/forum?id=YCWjhGrJFD |
|
[] | Poster | [] | Gaussian processes (GPs) are popular nonparametric statistical models for learning unknown functions and quantifying the spatiotemporal uncertainty in data. Recent works have extended GPs to model scalar and vector quantities distributed over non-Euclidean domains, including smooth manifolds, appearing in numerous fields such as computer vision, dynamical systems, and neuroscience. However, these approaches assume that the manifold underlying the data is known, limiting their practical utility. We introduce RVGP, a generalisation of GPs for learning vector signals over latent Riemannian manifolds. Our method uses positional encoding with eigenfunctions of the connection Laplacian, associated with the tangent bundle, readily derived from common graph-based approximation of data. We demonstrate that RVGP possesses global regularity over the manifold, which allows it to super-resolve and inpaint vector fields while preserving singularities. Furthermore, we use RVGP to reconstruct high-density neural dynamics derived from low-density EEG recordings in healthy individuals and Alzheimer's patients. We show that vector field singularities are important disease markers and that their reconstruction leads to a comparable classification accuracy of disease states to high-density recordings. Thus, our method overcomes a significant practical limitation in experimental and clinical applications. | [] | [] | Implicit Gaussian process representation of vector fields over arbitrary latent manifolds | [
"Robert Peach",
"Matteo Vinao-Carl",
"Nir Grossman",
"Michael David",
"Emma Mallas",
"David J. Sharp",
"Paresh A. Malhotra",
"Pierre Vandergheynst",
"Adam Gosztolai"
] | 2309.16746 | 18,431 | https://openreview.net/forum?id=YEPlTU5mZC |
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[] | Poster | [] | Agents navigating in 3D environments require some form of memory, which should hold a compact and actionable representation of the history of observations useful for decision taking and planning. In most end-to-end learning approaches the representation is latent and usually does not have a clearly defined interpretation, whereas classical robotics addresses this with scene reconstruction resulting in some form of map, usually estimated with geometry and sensor models and/or learning. In this work we propose to learn an actionable representation of the scene independently of the targeted downstream task and without explicitly optimizing reconstruction. The learned representation is optimized by a blind auxiliary agent trained to navigate with it on multiple short sub episodes branching out from a waypoint and, most importantly, without any direct visual observation. We argue and show that the blindness property is important and forces the (trained) latent representation to be the only means for planning. With probing experiments we show that the learned representation optimizes navigability and not reconstruction. On downstream tasks we show that it is robust to changes in distribution, in particular the sim2real gap, which we evaluate with a real physical robot in a real office building, significantly improving performance. | [] | [] | Learning with a Mole: Transferable latent spatial representations for navigation without reconstruction | [
"Guillaume Bono",
"Leonid Antsfeld",
"Assem Sadek",
"Gianluca Monaci",
"Christian Wolf"
] | 2306.03857 | 19,332 | https://openreview.net/forum?id=8HCARN2hhw |
|
[] | Poster | [
"https://github.com/microsoft/DPSDA"
] | Generating differentially private (DP) synthetic data that closely resembles the original private data is a scalable way to mitigate privacy concerns in the current data-driven world. In contrast to current practices that train customized models for this task, we aim to generate DP Synthetic Data via APIs (DPSDA), where we treat foundation models as blackboxes and only utilize their inference APIs. Such API-based, training-free approaches are easier to deploy as exemplified by the recent surge in the number of API-based apps. These approaches can also leverage the power of large foundation models which are only accessible via their inference APIs. However, this comes with greater challenges due to strictly more restrictive model access and the need to protect privacy from the API provider.In this paper, we present a new framework called Private Evolution (PE) to solve this problem and show its initial promise on synthetic images. Surprisingly, PE can match or even outperform state-of-the-art (SOTA) methods without any model training. For example, on CIFAR10 (with ImageNet as the public data), we achieve FID≤7.9 with privacy cost ε = 0.67, significantly improving the previous SOTA from ε = 32. We further demonstrate the promise of applying PE on large foundation models such as Stable Diffusion to tackle challenging private datasets with a small number of high-resolution images. | [] | [] | Differentially Private Synthetic Data via Foundation Model APIs 1: Images | [
"Zinan Lin",
"Sivakanth Gopi",
"Janardhan Kulkarni",
"Harsha Nori",
"Sergey Yekhanin"
] | 2305.15560 | 18,430 | https://openreview.net/forum?id=YEhQs8POIo |
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[] | Poster | [] | Unsupervised methods for dimensionality reduction of neural activity and behavior have provided unprecedented insights into the underpinnings of neural information processing. One popular approach involves the recurrent switching linear dynamical system (rSLDS) model, which describes the latent dynamics of neural spike train data using discrete switches between a finite number of low-dimensional linear dynamical systems. However, a few properties of rSLDS model limit its deployability on trial-varying data, such as a fixed number of states over trials, and no latent structure or organization of states. Here we overcome these limitations by endowing the rSLDS model with a semi-Markov discrete state process, with latent geometry, that captures key properties of stochastic processes over partitions with flexible state cardinality. We leverage partial differential equations (PDE) theory to derive an efficient, semi-parametric formulation for dynamical sufficient statistics to the discrete states. This process, combined with switching dynamics, defines our infinite recurrent switching linear dynamical system (irSLDS) model class. We first validate and demonstrate the capabilities of our model on synthetic data. Next, we turn to the analysis of mice electrophysiological data during decision-making, and uncover strong non-stationary processes underlying both within-trial and trial-averaged neural activity. | [] | [] | Parsing neural dynamics with infinite recurrent switching linear dynamical systems | [
"Victor Geadah",
"International Brain Laboratory",
"Jonathan W. Pillow"
] | 18,427 | https://openreview.net/forum?id=YIls9HEa52 |
||
[] | Spotlight Poster | [] | In this paper, we extend mean-field Langevin dynamics to minimax optimization over probability distributions for the first time with symmetric and provably convergent updates. We propose \emph{mean-field Langevin averaged gradient} (MFL-AG), a single-loop algorithm that implements gradient descent ascent in the distribution spaces with a novel weighted averaging, and establish average-iterate convergence to the mixed Nash equilibrium. We also study both time and particle discretization regimes and prove a new uniform-in-time propagation of chaos result which accounts for the dependency of the particle interactions on all previous distributions. Furthermore, we propose \emph{mean-field Langevin anchored best response} (MFL-ABR), a symmetric double-loop algorithm based on best response dynamics with linear last-iterate convergence. Finally, we study applications to zero-sum Markov games and conduct simulations demonstrating long-term optimality. | [] | [] | Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems | [
"Juno Kim",
"Kakei Yamamoto",
"Kazusato Oko",
"Zhuoran Yang",
"Taiji Suzuki"
] | 2312.01127 | 18,426 | https://openreview.net/forum?id=YItWKZci78 |
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[] | Poster | [] | Fair machine learning seeks to mitigate model prediction bias against certain demographic subgroups such as elder and female. Recently, fair representation learning (FRL) trained by deep neural networks has demonstrated superior performance, whereby representations containing no demographic information are inferred from the data and then used as the input to classification or other downstream tasks. Despite the development of FRL methods, their vulnerability under data poisoning attack, a popular protocol to benchmark model robustness under adversarial scenarios, is under-explored. Data poisoning attacks have been developed for classical fair machine learning methods which incorporate fairness constraints into shallow-model classifiers.Nonetheless, these attacks fall short in FRL due to notably different fairness goals and model architectures. This work proposes the first data poisoning framework attacking FRL. We induce the model to output unfair representations that contain as much demographic information as possible by injecting carefully crafted poisoning samples into the training data.This attack entails a prohibitive bilevel optimization, wherefore an effective approximated solution is proposed. A theoretical analysis on the needed number of poisoning samples is derived and sheds light on defending against the attack. Experiments on benchmark fairness datasets and state-of-the-art fair representation learning models demonstrate the superiority of our attack. | [] | [] | Towards Poisoning Fair Representations | [
"Tianci Liu",
"Haoyu Wang",
"Feijie Wu",
"Hengtong Zhang",
"Pan Li",
"Lu Su",
"Jing Gao"
] | 2309.16487 | 18,425 | https://openreview.net/forum?id=YLJs4mKJCF |
|
[] | Poster | [] | Inverse problems aim to determine causal factors from observations, a crucial task in engineering and science. Lately, generative models, especially diffusion models, have gained popularity in this area for their ability to produce realistic solutions and their good mathematical properties. Despite their success, an important drawback of diffusion models is their sensitivity to the choice of variance schedule, which controls the dynamics of the diffusion process. Fine-tuning this schedule for specific applications is crucial but time-costly, and does not guarantee an optimal result. We propose a novel approach for learning the schedule as part of the training process. Our method supports probabilistic conditioning on data, provides high-quality solutions, and is flexible, proving able to adapt to different applications with minimum overhead. This approach is tested in two unrelated inverse problems: super-resolution microscopy and quantitative phase imaging, yielding comparable or superior results to previous methods and fine-tuned diffusion models. We conclude that fine-tuning the schedule by experimentation should be avoided because it can be learned during training in a stable way that yields better results. | [] | [] | Conditional Variational Diffusion Models | [
"Gabriel Della Maggiora",
"Luis Alberto Croquevielle",
"Nikita Deshpande",
"Harry Horsley",
"Thomas Heinis",
"Artur Yakimovich"
] | 2312.02246 | 18,424 | https://openreview.net/forum?id=YOKnEkIuoi |
|
[] | Poster | [] | The predictions of Large Language Models (LLMs) on downstream tasks often improve significantly when including examples of the input–label relationship in the context. However, there is currently no consensus about how this in-context learning (ICL) ability of LLMs works. For example, while Xie et al. (2022) liken ICL to a general-purpose learning algorithm, Min et al. (2022b) argue ICL does not even learn label relationships from in-context examples. In this paper, we provide novel insights into how ICL leverages label information, revealing both capabilities and limitations. To ensure we obtain a comprehensive picture of ICL behavior, we study probabilistic aspects of ICL predictions and thoroughly examine the dynamics of ICL as more examples are provided. Our experiments show that ICL predictions almost always depend on in-context labels, and that ICL can learn truly novel tasks in-context. However, we also find that ICL struggles to fully overcome prediction preferences acquired from pre-training data, and, further, that ICL does not consider all in-context information equally. | [] | [] | In-Context Learning Learns Label Relationships but Is Not Conventional Learning | [
"Jannik Kossen",
"Yarin Gal",
"Tom Rainforth"
] | 2307.12375 | 18,423 | https://openreview.net/forum?id=YPIA7bgd5y |
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[] | Poster | [] | Multitask Reinforcement Learning (MTRL) approaches have gained increasing attention for its wide applications in many important Reinforcement Learning (RL) tasks. However, while recent advancements in MTRL theory have focused on the improved statistical efficiency by assuming a shared structure across tasks, exploration--a crucial aspect of RL--has been largely overlooked. This paper addresses this gap by showing that when an agent is trained on a sufficiently diverse set of tasks, a generic policy-sharing algorithm with myopic exploration design like $\epsilon$-greedy that are inefficient in general can be sample-efficient for MTRL. To the best of our knowledge, this is the first theoretical demonstration of the "exploration benefits" of MTRL. It may also shed light on the enigmatic success of the wide applications of myopic exploration in practice. To validate the role of diversity, we conduct experiments on synthetic robotic control environments, where the diverse task set aligns with the task selection by automatic curriculum learning, which is empirically shown to improve sample-efficiency. | [] | [] | Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks | [
"Ziping Xu",
"Zifan Xu",
"Runxuan Jiang",
"Peter Stone",
"Ambuj Tewari"
] | 2403.01636 | 18,421 | https://openreview.net/forum?id=YZrg56G0JV |
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[] | Spotlight Poster | [] | This paper introduces an efficient strategy to transform Large Language Models(LLMs) into Multi-Modal Large Language Models (MLLMs). By conceptualizingthis transformation as a domain adaptation process, i.e., transitioning from textunderstanding to embracing multiple modalities, we intriguingly note that, withineach attention block, tuning LayerNorm suffices to yield strong performance.Moreover, when benchmarked against other tuning approaches like full parameterfinetuning or LoRA, its benefits on efficiency are substantial. For example, whencompared to LoRA on a 13B model scale, performance can be enhanced by anaverage of over 20% across five multi-modal tasks, and meanwhile, results in asignificant reduction of trainable parameters by 41.9% and a decrease in GPUmemory usage by 17.6%. On top of this LayerNorm strategy, we showcase thatselectively tuning only with conversational data can improve efficiency further.Beyond these empirical outcomes, we provide a comprehensive analysis to explorethe role of LayerNorm in adapting LLMs to the multi-modal domain and improvingthe expressive power of the model. | [] | [] | Tuning LayerNorm in Attention: Towards Efficient Multi-Modal LLM Finetuning | [
"Bingchen Zhao",
"Haoqin Tu",
"Chen Wei",
"Jieru Mei",
"Cihang Xie"
] | 2312.11420 | 18,422 | https://openreview.net/forum?id=YR3ETaElNK |
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[] | Spotlight Poster | [] | Forward- or reverse-mode automatic differentiation (AD) is a popular algorithm for computing the derivative of a function expressed by a program. AD always outputs the correct derivative if a program does not use any non-differentiable functions and control flows; however, it may return an arbitrary value otherwise. In this work, we investigate what AD computes for neural networks that may contain non-differentiable functions such as ReLU and maxpools. We first prove that AD always returns a generalized derivative called a Clarke subderivative for networks with pointwise activation functions, if the minibatch size is one and all non-differentiable neurons have distinct bias parameters. We show that the same conclusion does not hold otherwise, but does hold under some mild sufficient conditions. We also prove similar results for more general networks that can use maxpools and bias parameters shared across different neurons. We empirically check our sufficient conditions over popular network architectures and observe that AD almost always computes a Clarke subderivative in practical learning setups. | [] | [] | What does automatic differentiation compute for neural networks? | [
"Sejun Park",
"Sanghyuk Chun",
"Wonyeol Lee"
] | 19,311 | https://openreview.net/forum?id=8vKknbgXxf |
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[] | Poster | [] | Diffusion models have impressive image generation capability, but low-quality generations still exist, and their identification remains challenging due to the lack of a proper sample-wise metric. To address this, we propose BayesDiff, a pixel-wise uncertainty estimator for generations from diffusion models based on Bayesian inference. In particular, we derive a novel uncertainty iteration principle to characterize the uncertainty dynamics in diffusion, and leverage the last-layer Laplace approximation for efficient Bayesian inference. The estimated pixel-wise uncertainty can not only be aggregated into a sample-wise metric to filter out low-fidelity images but also aids in augmenting successful generations and rectifying artifacts in failed generations in text-to-image tasks. Extensive experiments demonstrate the efficacy of BayesDiff and its promise for practical applications. | [] | [] | BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference | [
"Siqi Kou",
"Lei Gan",
"Dequan Wang",
"Chongxuan Li",
"Zhijie Deng"
] | 2310.11142 | 18,418 | https://openreview.net/forum?id=YcM6ofShwY |
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[] | Poster | [] | Self-supervised learning through contrastive representations is an emergent and promising avenue, aiming at alleviating the availability of labeled data. Recent research in the field also demonstrates its viability for several downstream tasks, henceforth leading to works that implement the contrastive principle through innovative loss functions and methods. However, despite achieving impressive progress, most methods depend on prohibitively large batch sizes and compute requirements for good performance. In this work, we propose the $\textbf{AUC}$-$\textbf{C}$ontrastive $\textbf{L}$earning, a new approach to contrastive learning that demonstrates robust and competitive performance in compute-limited regimes. We propose to incorporate the contrastive objective within the AUC-maximization framework, by noting that the AUC metric is maximized upon enhancing the probability of the network's binary prediction difference between positive and negative samples which inspires adequate embedding space arrangements in representation learning. Unlike standard contrastive methods, when performing stochastic optimization, our method maintains unbiased stochastic gradients and thus is more robust to batchsizes as opposed to standard stochastic optimization problems.Remarkably, our method with a batch size of 256, outperforms several state-of-the-art methods that may need much larger batch sizes (e.g., 4096), on ImageNet and other standard datasets. Experiments on transfer learning, few-shot learning, and other downstream tasks also demonstrate the viability of our method. | [] | [] | AUC-CL: A Batchsize-Robust Framework for Self-Supervised Contrastive Representation Learning | [
"Rohan Sharma",
"Kaiyi Ji",
"zhiqiang xu",
"Changyou Chen"
] | 18,415 | https://openreview.net/forum?id=YgMdDQB09U |
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[] | Poster | [] | Diffusion models have recently received increasing research attention for their impressive transfer abilities to semantic segmentation tasks. However, previous works rely on additional supervision to produce fine-grained segmentation maps, leaving it unclear how much diffusion models alone understand the semantic relations of their generated images. To help answer this question, we exploit the semantic knowledge extracted from Stable Diffusion (SD) and build an image segmentor that can generate fine-grained segmentation maps without any additional training. The major issue that makes this task challenging for previous works is that semantically meaningful feature maps usually exist only in the spatially lower-dimensional layers, which makes it infeasible to extract pixel-level semantic relations directly from the feature maps. To overcome this challenge, our framework identifies semantic correspondences between image pixels and spatial locations of low-dimensional feature maps by analyzing SD’s generation process and utilizes them to construct image-resolution segmentation maps. In extensive experiments, the produced segmentation maps are shown to be well delineated and capture detailed parts of the images, indicating the existence of highly accurate pixel-level semantic knowledge in the diffusion models | [] | [] | EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models | [
"Koichi Namekata",
"Amirmojtaba Sabour",
"Sanja Fidler",
"Seung Wook Kim"
] | 2401.11739 | 18,414 | https://openreview.net/forum?id=YqyTXmF8Y2 |
|
[] | Spotlight Poster | [
"https://github.com/Eleanor-H/MUSTARD"
] | Recent large language models (LLMs) have witnessed significant advancement in various tasks, including mathematical reasoning and theorem proving. As these two tasks require strict and formal multi-step inference, they are appealing domains for exploring the reasoning ability of LLMs but still face important challenges. Previous studies such as Chain-of-Thought (CoT) have revealed the effectiveness of intermediate steps guidance. However, such step-wise annotation requires heavy labor, leading to insufficient training steps for current benchmarks. To fill this gap, this work introduces MUSTARD, a data generation framework that masters uniform synthesis of theorem and proof data of high quality and diversity. MUSTARD synthesizes data in three stages: (1) It samples a few mathematical concept seeds as the problem category. (2) Then, it prompts a generative language model with the sampled concepts to obtain both the problems and their step-wise formal solutions. (3) Lastly, the framework utilizes a proof assistant (e.g., Lean Prover) to filter the valid proofs. With the proposed MUSTARD, we present a theorem-and-proof benchmark MUSTARDSAUCE with 5,866 valid data points. Each data point contains an informal statement, an informal proof, and a translated formal proof that passes the prover validation. We perform extensive analysis and demonstrate that MUSTARD generates validated high-quality step-by-step data. We further apply the MUSTARDSAUCE for fine-tuning smaller language models. The fine-tuned Llama 2-7B achieves a 15.41% average relative performance gain in automated theorem proving, and 8.18% in math word problems. Codes and data are available at https://github.com/Eleanor-H/MUSTARD. | [] | [] | MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data | [
"Yinya Huang",
"Xiaohan Lin",
"Zhengying Liu",
"Qingxing Cao",
"Huajian Xin",
"Haiming Wang",
"Zhenguo Li",
"Linqi Song",
"Xiaodan Liang"
] | 2402.08957 | 19,310 | https://openreview.net/forum?id=8xliOUg9EW |
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[] | Poster | [] | We study the cost of overfitting in noisy kernel ridge regression (KRR), which we define as the ratio between the test error of the interpolating ridgeless model and the test error of the optimally-tuned model. We take an ``agnostic'' view in the following sense: we consider the cost as a function of sample size for any target function, even if the sample size is not large enough for consistency or the target is outside the RKHS. We analyze the cost of overfitting under a Gaussian universality ansatz using recently derived (non-rigorous) risk estimates in terms of the task eigenstructure. Our analysis provides a more refined characterization of benign, tempered and catastrophic overfitting (cf. Mallinar et al. 2022). | [] | [] | An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression | [
"Lijia Zhou",
"James B Simon",
"Gal Vardi",
"Nathan Srebro"
] | 2306.13185 | 18,413 | https://openreview.net/forum?id=YrTI2Zu0dd |
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[] | Poster | [] | We propose conditional flows of the maximum mean discrepancy (MMD) with the negative distance kernel for posterior sampling and conditional generative modelling. This MMD, which is also known as energy distance, has several advantageous properties like efficient computation via slicing and sorting. We approximate the joint distribution of the ground truth and the observations using discrete Wasserstein gradient flows and establish an error bound for the posterior distributions. Further, we prove that our particle flow is indeed a Wasserstein gradient flow of an appropriate functional. The power of our method is demonstrated by numerical examples including conditional image generation and inverse problems like superresolution, inpainting and computed tomography in low-dose and limited-angle settings. | [] | [] | Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel | [
"Paul Hagemann",
"Johannes Hertrich",
"Fabian Altekrüger",
"Robert Beinert",
"Jannis Chemseddine",
"Gabriele Steidl"
] | 2310.03054 | 18,412 | https://openreview.net/forum?id=YrXHEb2qMb |
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[] | Spotlight Poster | [] | Despite the notable advancements in deep reinforcement learning (DRL) in recent years, a prevalent issue that is often overlooked is the impact of signal delay. Signal delay occurs when there is a lag between an agent's perception of the environment and its corresponding actions. In this paper, we first formalize delayed-observation Markov decision processes (DOMDP) by extending the standard MDP framework to incorporate signal delays. Next, we elucidate the challenges posed by the presence of signal delay in DRL, showing that trivial DRL algorithms and generic methods for partially observable tasks suffer greatly from delays. Lastly, we propose effective strategies to overcome these challenges. Our methods achieve remarkable performance in continuous robotic control tasks with large delays, yielding results comparable to those in non-delayed cases. Overall, our work contributes to a deeper understanding of DRL in the presence of signal delays and introduces novel approaches to address the associated challenges. | [] | [] | Addressing Signal Delay in Deep Reinforcement Learning | [
"Wei Wang",
"Dongqi Han",
"Xufang Luo",
"Dongsheng Li"
] | 18,410 | https://openreview.net/forum?id=Z8UfDs4J46 |
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[] | Poster | [] | Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world. Drawing inspiration from neuroscience, we develop the Information-Theoretic Hierarchical Perception (ITHP) model, which utilizes the concept of information bottleneck. Different from most traditional fusion models that incorporate all modalities identically in neural networks, our model designates a prime modality and regards the remaining modalities as detectors in the information pathway, serving to distill the flow of information. Our proposed perception model focuses on constructing an effective and compact information flow by achieving a balance between the minimization of mutual information between the latent state and the input modal state, and the maximization of mutual information between the latent states and the remaining modal states. This approach leads to compact latent state representations that retain relevant information while minimizing redundancy, thereby substantially enhancing the performance of multimodal representation learning. Experimental evaluations on the MUStARD, CMU-MOSI, and CMU-MOSEI datasets demonstrate that our model consistently distills crucial information in multimodal learning scenarios, outperforming state-of-the-art benchmarks. Remarkably, on the CMU-MOSI dataset, ITHP-DeBERTa surpasses human-level performance in the multimodal sentiment binary classification task across all evaluation metrics (i.e., Binary Accuracy, F1 Score, Mean Absolute Error, and Pearson Correlation). | [] | [] | Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning | [
"Xiongye Xiao",
"Gengshuo Liu",
"Gaurav Gupta",
"Defu Cao",
"Shixuan Li",
"Yaxing Li",
"Tianqing Fang",
"Mingxi Cheng",
"Paul Bogdan"
] | 2404.09403 | 18,409 | https://openreview.net/forum?id=Z9AZsU1Tju |
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[] | Poster | [] | This paper introduces a new method for minimizing matrix-smooth non-convex objectives through the use of novel Compressed Gradient Descent (CGD) algorithms enhanced with a matrix-valued stepsize. The proposed algorithms are theoretically analyzed first in the single-node and subsequently in the distributed settings. Our theoretical results reveal that the matrix stepsize in CGD can capture the objective’s structure and lead to faster convergence compared to a scalar stepsize. As a byproduct of our general results, we emphasize the importance of selecting the compression mechanism and the matrix stepsize in a layer-wise manner, taking advantage of model structure. Moreover, we provide theoretical guarantees for free compression, by designing specific layer-wise compressors for the non-convex matrix smooth objectives. Our findings are supported with empirical evidence. | [] | [] | Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization | [
"Hanmin Li",
"Avetik Karagulyan",
"Peter Richtárik"
] | 18,407 | https://openreview.net/forum?id=ZEZ0CPmoSI |
||
[] | Poster | [] | Learning from off-policy data is essential for sample-efficient reinforcement learning. In the present work, we build on the insight that the advantage function can be understood as the causal effect of an action on the return, and show that this allows us to decompose the return of a trajectory into parts caused by the agent’s actions (skill) and parts outside of the agent’s control (luck). Furthermore, this decomposition enables us to naturally extend Direct Advantage Estimation (DAE) to off-policy settings (Off-policy DAE). The resulting method can learnfrom off-policy trajectories without relying on importance sampling techniques or truncating off-policy actions. We draw connections between Off-policy DAE and previous methods to demonstrate how it can speed up learning and when the proposed off-policy corrections are important. Finally, we use the MinAtar environments to illustrate how ignoring off-policy corrections can lead to suboptimal policy optimization performance. | [] | [] | Skill or Luck? Return Decomposition via Advantage Functions | [
"Hsiao-Ru Pan",
"Bernhard Schölkopf"
] | 2402.12874 | 18,406 | https://openreview.net/forum?id=ZFMiHfZwIf |
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[] | Poster | [] | Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. For example in the retina, functional types can be identified by a carefully selected and manually curated battery of stimuli. However, this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Maximally Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. Presenting maximally discriminative stimuli during data acquisition allows for on-the-fly assignment to functional cell types, and paves the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. We will make our code avail- able online upon publication. | [] | [] | Most discriminative stimuli for functional cell type clustering | [
"Max F Burg",
"Thomas Zenkel",
"Michaela Vystrčilová",
"Jonathan Oesterle",
"Larissa Höfling",
"Konstantin Friedrich Willeke",
"Jan Lause",
"Sarah Müller",
"Paul G. Fahey",
"Zhiwei Ding",
"Kelli Restivo",
"Shashwat Sridhar",
"Tim Gollisch",
"Philipp Berens",
"Andreas S. Tolias",
"Thomas Euler",
"Matthias Bethge",
"Alexander S Ecker"
] | 2401.05342 | 19,293 | https://openreview.net/forum?id=9W6KaAcYlr |
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[] | Poster | [] | Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort. To automate this process, in this paper, we propose a novel framework for discrete prompt optimization, called EvoPrompt, which borrows the idea of evolutionary algorithms (EAs) as they exhibit good performance and fast convergence. To enable EAs to work on discrete prompts, which are natural language expressions that need to be coherent and human-readable, we connect LLMs with EAs. This approach allows us to simultaneously leverage the powerful language processing capabilities of LLMs and the efficient optimization performance of EAs. Specifically, abstaining from any gradients or parameters, EvoPrompt starts from a population of prompts and iteratively generates new prompts with LLMs based on the evolutionary operators, improving the population based on the development set. We optimize prompts for both closed- and open-source LLMs including GPT-3.5 and Alpaca, on 31 datasets covering language understanding, generation tasks, as well as BIG-Bench Hard (BBH) tasks. EvoPrompt significantly outperforms human-engineered prompts and existing methods for automatic prompt generation (e.g., up to 25% on BBH). Furthermore, EvoPrompt demonstrates that connecting LLMs with EAs creates synergies, which could inspire further research on the combination of LLMs and conventional algorithms. | [] | [] | Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers | [
"Qingyan Guo",
"Rui Wang",
"Junliang Guo",
"Bei Li",
"Kaitao Song",
"Xu Tan",
"Guoqing Liu",
"Jiang Bian",
"Yujiu Yang"
] | 2309.08532 | 18,405 | https://openreview.net/forum?id=ZG3RaNIsO8 |
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[] | Poster | [] | Large language models (LLMs) have demonstrated impressive reasoning abilities in complex tasks. However, they lack up-to-date knowledge and experience hallucinations during reasoning, which can lead to incorrect reasoning processes and diminish their performance and trustworthiness. Knowledge graphs (KGs), which capture vast amounts of facts in a structured format, offer a reliable source of knowledge for reasoning. Nevertheless, existing KG-based LLM reasoning methods only treat KGs as factual knowledge bases and overlook the importance of their structural information for reasoning. In this paper, we propose a novel method called reasoning on graphs (RoG) that synergizes LLMs with KGs to enable faithful and interpretable reasoning. Specifically, we present a planning-retrieval-reasoning framework, where RoG first generates relation paths grounded by KGs as faithful plans. These plans are then used to retrieve valid reasoning paths from the KGs for LLMs to conduct faithful reasoning. Furthermore, RoG not only distills knowledge from KGs to improve the reasoning ability of LLMs through training but also allows seamless integration with any arbitrary LLMs during inference. Extensive experiments on two benchmark KGQA datasets demonstrate that RoG achieves state-of-the-art performance on KG reasoning tasks and generates faithful and interpretable reasoning results. | [] | [] | Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning | [
"LINHAO LUO",
"Yuan-Fang Li",
"Reza Haf",
"Shirui Pan"
] | 2310.01061 | 18,404 | https://openreview.net/forum?id=ZGNWW7xZ6Q |
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[] | Spotlight Poster | [] | In federated learning (FL), clients usually have diverse participation statistics that are unknown a priori, which can significantly harm the performance of FL if not handled properly. Existing works aiming at addressing this problem are usually based on global variance reduction, which requires a substantial amount of additional memory in a multiplicative factor equal to the total number of clients. An important open problem is to find a lightweight method for FL in the presence of clients with unknown participation rates. In this paper, we address this problem by adapting the aggregation weights in federated averaging (FedAvg) based on the participation history of each client. We first show that, with heterogeneous participation statistics, FedAvg with non-optimal aggregation weights can diverge from the optimal solution of the original FL objective, indicating the need of finding optimal aggregation weights. However, it is difficult to compute the optimal weights when the participation statistics are unknown. To address this problem, we present a new algorithm called FedAU, which improves FedAvg by adaptively weighting the client updates based on online estimates of the optimal weights without knowing the statistics of client participation. We provide a theoretical convergence analysis of FedAU using a novel methodology to connect the estimation error and convergence. Our theoretical results reveal important and interesting insights, while showing that FedAU converges to an optimal solution of the original objective and has desirable properties such as linear speedup. Our experimental results also verify the advantage of FedAU over baseline methods with various participation patterns. | [] | [] | A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging | [
"Shiqiang Wang",
"Mingyue Ji"
] | 2306.03401 | 18,403 | https://openreview.net/forum?id=ZKEuFKfCKA |
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[] | Poster | [] | While graph neural networks (GNNs) are widely used for node and graph representation learning tasks, the reliability of GNN uncertainty estimates under distribution shifts remains relatively under-explored. Indeed, while \textit{post-hoc} calibration strategies can be used to improve in-distribution calibration, they need not also improve calibration under distribution shift. However, techniques which produce GNNs with better \textit{intrinsic} uncertainty estimates are particularly valuable, as whey can always be combined with post-hoc strategies later. Therefore, in this work, we propose G-$\Delta$UQ, a novel training framework designed to improve intrinsic GNN uncertainty estimates. Our framework adapts the principle of stochastic data centering to graph data through novel graph anchoring strategies, and is able to support partially stochastic GNNs. While, the prevalent wisdom is that fully stochastic networks are necessary to obtain reliable estimates, we find that the structure induced by our anchoring strategies renders this unnecessary and allows us to support \gduq~ on pretrained models. Indeed, through extensive evaluation under covariate, concept and graph size shifts, we show that G-$\Delta$UQ leads to better calibrated GNNs for node and graph classification. Further, it also improves performance on other uncertainty-based tasks like out-of-distribution detection and generalization gap estimation. Overall, our work provides insights into uncertainty estimation for GNNs, and demonstrates the utility of G-$\Delta$UQ in obtaining reliable estimates. | [] | [] | Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks | [
"Puja Trivedi",
"Mark Heimann",
"Rushil Anirudh",
"Danai Koutra",
"Jayaraman J. Thiagarajan"
] | 2309.10976 | 18,402 | https://openreview.net/forum?id=ZL6yd6N1S2 |
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[] | Poster | [] | Recent advances in the theory of Neural Operators (NOs) have enabled fast and accurate computation of the solutions to complex systems described by partial differential equations (PDEs). Despite their great success, current NO-based solutions face important challenges when dealing with spatio-temporal PDEs over long time scales. Specifically, the current theory of NOs does not present a systematic framework to perform data assimilation and efficiently correct the evolution of PDE solutions over time based on sparsely sampled noisy measurements. In this paper, we propose a learning-based state-space approach to compute the solution operators to infinite-dimensional semilinear PDEs. Exploiting the structure of semilinear PDEs and the theory of nonlinear observers in function spaces, we develop a flexible recursive method that allows for both prediction and data assimilation by combining prediction and correction operations. The proposed framework is capable of producing fast and accurate predictions over long time horizons, dealing with irregularly sampled noisy measurements to correct the solution, and benefits from the decoupling between the spatial and temporal dynamics of this class of PDEs. We show through experiments on the Kuramoto-Sivashinsky, Navier-Stokes and Korteweg-de Vries equations that the proposed model is robust to noise and can leverage arbitrary amounts of measurements to correct its prediction over a long time horizon with little computational overhead. | [] | [] | Learning semilinear neural operators: A unified recursive framework for prediction and data assimilation. | [
"Ashutosh Singh",
"Ricardo Augusto Borsoi",
"Deniz Erdogmus",
"Tales Imbiriba"
] | 2402.15656 | 18,401 | https://openreview.net/forum?id=ZMv6zKYYUs |
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[] | Poster | [] | Generating complete 360\textdegree{} panoramas from narrow field of view images is ongoing research as omnidirectional RGB data is not readily available. Existing GAN-based approaches face some barriers to achieving higher quality output, and have poor generalization performance over different mask types. In this paper, we present our 360\textdegree{} indoor RGB panorama outpainting model using latent diffusion models (LDM), called PanoDiffusion. We introduce a new bi-modal latent diffusion structure that utilizes both RGB and depth panoramic data during training, which works surprisingly well to outpaint depth-free RGB images during inference. We further propose a novel technique of introducing progressive camera rotations during each diffusion denoising step, which leads to substantial improvement in achieving panorama wraparound consistency. Results show that our PanoDiffusion not only significantly outperforms state-of-the-art methods on RGB panorama outpainting by producing diverse well-structured results for different types of masks, but can also synthesize high-quality depth panoramas to provide realistic 3D indoor models. | [] | [] | PanoDiffusion: 360-degree Panorama Outpainting via Diffusion | [
"Tianhao Wu",
"Chuanxia Zheng",
"Tat-Jen Cham"
] | 2307.03177 | 18,400 | https://openreview.net/forum?id=ZNzDXDFZ0B |
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[] | Poster | [] | Quantifying the impact of training data points is crucial for understanding the outputs of machine learning models and for improving the transparency of the AI pipeline. The influence function is a principled and popular data attribution method, but its computational cost often makes it challenging to use. This issue becomes more pronounced in the setting of large language models and text-to-image models. In this work, we propose DataInf, an efficient influence approximation method that is practical for large-scale generative AI models. Leveraging an easy-to-compute closed-form expression, DataInf outperforms existing influence computation algorithms in terms of computational and memory efficiency. Our theoretical analysis shows that DataInf is particularly well-suited for parameter-efficient fine-tuning techniques such as LoRA. Through systematic empirical evaluations, we show that DataInf accurately approximates influence scores and is orders of magnitude faster than existing methods. In applications to RoBERTa-large, Llama-2-13B-chat, and stable-diffusion-v1.5 models, DataInf effectively identifies the most influential fine-tuning examples better than other approximate influence scores. Moreover, it can help to identify which data points are mislabeled. | [] | [] | DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models | [
"Yongchan Kwon",
"Eric Wu",
"Kevin Wu",
"James Zou"
] | 2310.00902 | 19,284 | https://openreview.net/forum?id=9m02ib92Wz |
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[] | Spotlight Poster | [] | The fundamental success of large language models hinges upon the efficacious implementation of large-scale distributed training techniques. Nevertheless, building a vast, high-performance cluster featuring high-speed communication interconnectivity is prohibitively costly, and accessible only to prominent entities. In this work, we aim to lower this barrier and democratize large-scale training with limited bandwidth clusters. We propose a new approach called CO2 that introduces local-updating and asynchronous communication to the distributed data-parallel training, thereby facilitating the full overlap of COmunication with COmputation. CO2 is able to attain a remarkable 100\% scalability even on extensive multi-node clusters constrained by very limited communication bandwidth. We further propose the staleness gap penalty and outer momentum clipping techniques together with CO2 to bolster its convergence and training stability. Besides, CO2 exhibits seamless integration with well-established ZeRO-series optimizers which mitigate memory consumption of model states with large model training. We also provide a mathematical proof of convergence, accompanied by the establishment of a stringent upper bound. Furthermore, we validate our findings through an extensive set of practical experiments encompassing a wide range of tasks in the fields of computer vision and natural language processing. These experiments serve to demonstrate the capabilities of CO2 in terms of convergence, generalization, and scalability when deployed across configurations comprising up to 128 A100 GPUs. The outcomes emphasize the outstanding capacity of CO2 to achieve perfect 100\% scalability, no matter on clusters with 800Gbps RDMA or 80Gbps TCP/IP inter-node connections. | [] | [] | CO2: Efficient Distributed Training with Full Communication-Computation Overlap | [
"Weigao Sun",
"Zhen Qin",
"Weixuan Sun",
"Shidi Li",
"Dong Li",
"Xuyang Shen",
"Yu Qiao",
"Yiran Zhong"
] | 2401.16265 | 18,399 | https://openreview.net/forum?id=ZO5cn4IfaN |
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[] | Poster | [] | Retrieval-augmented language models (RALMs) hold promise to produce language understanding systems that are are factual, efficient, and up-to-date. An important desideratum of RALMs, is that retrieved information helps model performance when it is relevant, and does not harm performance when it is not. This is particularly important in multi-hop reasoning scenarios, where misuse of irrelevant evidence can lead to cascading errors. However, recent work has shown that retrieval augmentation can sometimes have a negative effect on performance. In this work, we present a thorough analysis on five open-domain question answering benchmarks, characterizing cases when retrieval reduces accuracy. We then propose two methods to mitigate this issue. First, a simple baseline that filters out retrieved passages that do not entail question-answer pairs according to a natural language inference (NLI) model. This is effective in preventing performance reduction, but at a cost of also discarding relevant passages. Thus, we propose a method for automatically generating data to fine-tune the language model to properly leverage retrieved passages, using a mix of relevant and irrelevant contexts at training time. We empirically show that even 1,000 examples suffice to train the model to be robust to irrelevant contexts while maintaining high performance on examples with relevant ones. | [] | [] | Making Retrieval-Augmented Language Models Robust to Irrelevant Context | [
"Ori Yoran",
"Tomer Wolfson",
"Ori Ram",
"Jonathan Berant"
] | 2310.01558 | 18,397 | https://openreview.net/forum?id=ZS4m74kZpH |
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[] | Poster | [] | Offline reinforcement learning (RL), which aims to fully explore offline datasets for training without interaction with environments, has attracted growing recent attention. A major challenge for the real-world application of offline RL stems from the robustness against state observation perturbations, e.g., as a result of sensor errors or adversarial attacks. Unlike online robust RL, agents cannot be adversarially trained in the offline setting. In this work, we propose Diffusion Model-Based Predictor (DMBP) in a new framework that recovers the actual states with conditional diffusion models for state-based RL tasks. To mitigate the error accumulation issue in model-based estimation resulting from the classical training of conventional diffusion models, we propose a non-Markovian training objective to minimize the sum entropy of denoised states in RL trajectory. Experiments on standard benchmark problems demonstrate that DMBP can significantly enhance the robustness of existing offline RL algorithms against different scales of ran- dom noises and adversarial attacks on state observations. Further, the proposed framework can effectively deal with incomplete state observations with random combinations of multiple unobserved dimensions in the test. Our implementation is available at https://github.com/zhyang2226/DMBP. | [] | [] | DMBP: Diffusion model-based predictor for robust offline reinforcement learning against state observation perturbations | [
"Zhihe YANG",
"Yunjian Xu"
] | 18,394 | https://openreview.net/forum?id=ZULjcYLWKe |
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[] | Poster | [
"https://github.com/xuangu-fang/Functional-Bayesian-Tucker-Decomposition"
] | Tucker decomposition is a powerful tensor model to handle multi-aspect data. It demonstrates the low-rank property by decomposing the grid-structured data as interactions between a core tensor and a set of object representations (factors). A fundamental assumption of such decomposition is that there were finite objects in each aspect or mode, corresponding to discrete indexes of data entries. However, many real-world data are not naturally posed in the setting. For example, geographic data is represented as continuous indexes of latitude and longitude coordinates, and cannot fit tensor models directly. To generalize Tucker decomposition to such scenarios, we propose FunBaT: Functional Bayesian Tucker Decomposition. We treat the continuous-indexed data as the interaction between the Tucker core and a group of latent functions. We use Gaussian processes (GP) as functional priors to model the latent functions, and then convert the GPs into state-space priors by constructing equivalent stochastic differential equations (SDE) to reduce computational cost. An efficient inference algorithm is further developed for scalable posterior approximation based on advanced message-passing techniques. The advantage of our method is shown in both synthetic data and several real-world applications. | [] | [] | Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data | [
"Shikai Fang",
"Xin Yu",
"Zheng Wang",
"Shibo Li",
"Mike Kirby",
"Shandian Zhe"
] | 2311.04829 | 18,393 | https://openreview.net/forum?id=ZWyZeqE928 |
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[] | Poster | [] | Lip-to-speech involves generating a natural-sounding speech synchronized with a soundless video of a person talking. Despite recent advances, current methods still cannot produce high-quality speech with high levels of intelligibility for challenging and realistic datasets such as LRS3. In this work, we present LipVoicer, a novel method that generates high-quality speech, even for in-the-wild and rich datasets, by incorporating the text modality. Given a silent video, we first predict the spoken text using a pre-trained lip-reading network. We then condition a diffusion model on the video and use the extracted text through a classifier-guidance mechanism where a pre-trained automatic speech recognition (ASR ) serves as the classifier. LipVoicer outperforms multiple lip-to-speech baselines on LRS2 and LRS3, which are in-the-wild datasets with hundreds of unique speakers in their test set and an unrestricted vocabulary. Moreover, our experiments show that the inclusion of the text modality plays a major role in the intelligibility of the produced speech, readily perceptible while listening, and is empirically reflected in the substantial reduction of the word error rate ( WER ) metric. We demonstrate the effectiveness of LipVoicer through human evaluation, which shows that it produces more natural and synchronized speech signals compared to competing methods. Finally, we created a demo showcasing LipVoicer’s superiority in producing natural, synchronized, and intelligible speech, providing additional evidence of its effectiveness. Project page: https://lipvoicer.github.io | [] | [] | LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading | [
"Yochai Yemini",
"Aviv Shamsian",
"Lior Bracha",
"Sharon Gannot",
"Ethan Fetaya"
] | 2306.03258 | 18,390 | https://openreview.net/forum?id=ZZCPSC5OgD |
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[] | Poster | [
"https://github.com/AI4Science-WestlakeU/beno.git"
] | Elliptic partial differential equations (PDEs) are a major class of time-independent PDEs that play a key role in many scientific and engineering domains such as fluid dynamics, plasma physics, and solid mechanics. Recently, neural operators have emerged as a promising technique to solve elliptic PDEs more efficiently by directly mapping the input to solutions. However, existing networks typically neglect complex geometries and inhomogeneous boundary values present in the real world. Here we introduce Boundary-Embedded Neural Operators (BENO), a novel neural operator architecture that embeds the complex geometries and inhomogeneous boundary values into the solving of elliptic PDEs. Inspired by classical Green's function, BENO consists of two Graph Neural Networks (GNNs) for interior source term and boundary values, respectively. Furthermore, a Transformer encoder maps the global boundary geometry into a latent vector which influences each message passing layer of the GNNs. We test our model and strong baselines extensively in elliptic PDEs with complex boundary conditions. We show that all existing baseline methods fail to learn the solution operator. In contrast, our model, endowed with boundary-embedded architecture, outperforms state-of-the-art neural operators and strong baselines by an average of 60.96%. | [] | [] | BENO: Boundary-embedded Neural Operators for Elliptic PDEs | [
"Haixin Wang",
"Jiaxin LI",
"Anubhav Dwivedi",
"Kentaro Hara",
"Tailin Wu"
] | 2401.09323 | 18,389 | https://openreview.net/forum?id=ZZTkLDRmkg |
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