mtybilly/Instruct2DS huggingface dataset:
Bifurcations and loss jumps in RNN training
Authors: Zahra Monfared, Niclas Göring, Daniel Durstewitz Lukas Eisenmann
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Recurrent neural networks (RNNs) are popular machine learning tools for modeling and forecasting sequential data and for inferring dynamical systems (DS) from observed time series. Concepts from DS theory (DST) have variously been used to further our understanding of both, how trained RNNs solve complex tasks, and the training process itself. Bifurcations are particularly important phenomena in DS, including RNNs, that refer to topological (qualitative) changes in a system's dynamical behavior as one or more of its parameters are varied. Knowing the bifurcation structure of an RNN will thus allow to deduce many of its computational and dynamical properties, like its sensitivity to parameter variations or its behavior during training. In particular, bifurcations may account for sudden loss jumps observed in RNN training that could severely impede the training process. Here we first mathematically prove for a particular class of ReLU-based RNNs that certain bifurcations are indeed associated with loss gradients tending toward infinity or zero. We then introduce a novel heuristic algorithm for detecting all fixed points and $k$-cycles in ReLU-based RNNs and their existence and stability regions, hence bifurcation manifolds in parameter space. In contrast to previous numerical algorithms for finding fixed points and common continuation methods, our algorithm provides $\textit{exact}$ results and returns fixed points and cycles up to high orders with surprisingly good scaling behavior. We exemplify the algorithm on the analysis of the training process of RNNs, and find that the recently introduced technique of generalized teacher forcing completely avoids certain types of bifurcations in training. Thus, besides facilitating the DST analysis of trained RNNs, our algorithm provides a powerful instrument for analyzing the training process itself.
Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes
Authors: Rishi Rajalingham, Mehrdad Jazayeri, Guangyu Robert Yang Aran Nayebi
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Humans and animals have a rich and flexible understanding of the physical world, which enables them to infer the underlying dynamical trajectories of objects and events, plausible future states, and use that to plan and anticipate the consequences of actions.However, the neural mechanisms underlying these computations are unclear.We combine a goal-driven modeling approach with dense neurophysiological data and high-throughput human behavioral readouts that contain thousands of comparisons to directly impinge on this question.Specifically, we construct and evaluate several classes of sensory-cognitive networks to predict the future state of rich, ethologically-relevant environments, ranging from self-supervised end-to-end models with pixel-wise or object-slot objectives, to models that future predict in the latent space of purely static image-pretrained or dynamic video-pretrained foundation models.We find that ``scale is \emph{not} all you need'', and that many state-of-the-art machine learning models fail to perform well on our neural and behavioral benchmarks for future prediction.In fact, only one class of models matches these data well overall.We find that neural responses are currently best predicted by models trained to predict the future state of their environment in the \emph{latent} space of pretrained foundation models optimized for \emph{dynamic} scenes in a self-supervised manner.These models also approach the neurons' ability to predict the environmental state variables that are visually hidden from view, despite not being explicitly trained to do so.Finally, we find that not all foundation model latents are equal.Notably, models that future predict in the latent space of video foundation models that are optimized to support a \emph{diverse} range of egocentric sensorimotor tasks, reasonably match \emph{both} human behavioral error patterns and neural dynamics across all environmental scenarios that we were able to test.Overall, these findings suggest that the neural mechanisms and behaviors of primate mental simulation have strong inductive biases associated with them, and are thus far most consistent with being optimized to future predict on \emph{reusable} visual representations that are useful for Embodied AI more generally.
Learning Visual Prior via Generative Pre-Training
Authors: Kai Ye, Yudong Li, Yuexiang Li, Kevin Qinghong Lin, Yefeng Zheng, Linlin Shen, Mike Zheng Shou Jinheng Xie
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Various stuff and things in visual data possess specific traits, which can be learned by deep neural networks and are implicitly represented as the visual prior, e.g., object location and shape, in the model. Such prior potentially impacts many vision tasks. For example, in conditional image synthesis, spatial conditions failing to adhere to the prior can result in visually inaccurate synthetic results. This work aims to explicitly learn the visual prior and enable the customization of sampling. Inspired by advances in language modeling, we propose to learn Visual prior via Generative Pre-Training, dubbed VisorGPT. By discretizing visual locations, e.g., bounding boxes, human pose, and instance masks, into sequences, VisorGPT can model visual prior through likelihood maximization. Besides, prompt engineering is investigated to unify various visual locations and enable customized sampling of sequential outputs from the learned prior. Experimental results demonstrate the effectiveness of VisorGPT in modeling visual prior and extrapolating to novel scenes, potentially motivating that discrete visual locations can be integrated into the learning paradigm of current language models to further perceive visual world. Code is available at https://sierkinhane.github.io/visor-gpt.
Operator Learning with Neural Fields: Tackling PDEs on General Geometries
Authors: Lise Le Boudec, Armand Kassaï Koupaï, Thomas X Wang, Yuan Yin, Jean-Noël Vittaut, Patrick Gallinari Louis Serrano
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Machine learning approaches for solving partial differential equations require learning mappings between function spaces. While convolutional or graph neural networks are constrained to discretized functions, neural operators present a promising milestone toward mapping functions directly. Despite impressive results they still face challenges with respect to the domain geometry and typically rely on some form of discretization. In order to alleviate such limitations, we present CORAL, a new method that leverages coordinate-based networks for solving PDEs on general geometries. CORAL is designed to remove constraints on the input mesh, making it applicable to any spatial sampling and geometry. Its ability extends to diverse problem domains, including PDE solving, spatio-temporal forecasting, and inverse problems like geometric design. CORAL demonstrates robust performance across multiple resolutions and performs well in both convex and non-convex domains, surpassing or performing on par with state-of-the-art models.
Learning Dense Flow Field for Highly-accurate Cross-view Camera Localization
Authors: ze xianghui, Jianfeng Lu, Yujiao Shi Zhenbo Song
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: This paper addresses the problem of estimating the 3-DoF camera pose for a ground-level image with respect to a satellite image that encompasses the local surroundings. We propose a novel end-to-end approach that leverages the learning of dense pixel-wise flow fields in pairs of ground and satellite images to calculate the camera pose. Our approach differs from existing methods by constructing the feature metric at the pixel level, enabling full-image supervision for learning distinctive geometric configurations and visual appearances across views. Specifically, our method employs two distinct convolution networks for ground and satellite feature extraction. Then, we project the ground feature map to the bird's eye view (BEV) using a fixed camera height assumption to achieve preliminary geometric alignment. To further establish the content association between the BEV and satellite features, we introduce a residual convolution block to refine the projected BEV feature. Optical flow estimation is performed on the refined BEV feature map and the satellite feature map using flow decoder networks based on RAFT. After obtaining dense flow correspondences, we apply the least square method to filter matching inliers and regress the ground camera pose. Extensive experiments demonstrate significant improvements compared to state-of-the-art methods. Notably, our approach reduces the median localization error by 89\%, 19\%, 80\%, and 35\% on the KITTI, Ford multi-AV, VIGOR, and Oxford RobotCar datasets, respectively.
Spatially Resolved Gene Expression Prediction from Histology Images via Bi-modal Contrastive Learning
Authors: Kuan Pang, Sai Chung, Catia Perciani, Sonya MacParland, Bo Wang, Gary Bader Ronald Xie
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Histology imaging is an important tool in medical diagnosis and research, enabling the examination of tissue structure and composition at the microscopic level. Understanding the underlying molecular mechanisms of tissue architecture is critical in uncovering disease mechanisms and developing effective treatments.Gene expression profiling provides insight into the molecular processes underlying tissue architecture, but the process can be time-consuming and expensive. We present BLEEP (Bi-modaL Embedding for Expression Prediction), a bi-modal embedding framework capable of generating spatially resolved gene expression profiles of whole-slide Hematoxylin and eosin (H&E) stained histology images. BLEEP uses contrastive learning to construct a low-dimensional joint embedding space from a reference dataset using paired image and expression profiles at micrometer resolution. With this approach, the gene expression of any query image patch can be imputed using the expression profiles from the reference dataset. We demonstrate BLEEP’s effectiveness in gene expression prediction by benchmarking its performance on a human liver tissue dataset captured using the 10x Visium platform, where it achieves significant improvements over existing methods. Our results demonstrate the potential of BLEEP to provide insights into the molecular mechanisms underlying tissue architecture, with important implications in diagnosis and research of various diseases. The proposed approach can significantly reduce the time and cost associated with gene expression profiling, opening up new avenues for high-throughput analysis of histology images for both research and clinical applications.
Data Augmentations for Improved (Large) Language Model Generalization
Authors: Yoav Wald, Claudia Shi, Suchi Saria, David M. Blei Amir Feder
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: The reliance of text classifiers on spurious correlations can lead to poor generalization at deployment, raising concerns about their use in safety-critical domains such as healthcare. In this work, we propose to use counterfactual data augmentation, guided by knowledge of the causal structure of the data, to simulate interventions on spurious features and to learn more robust text classifiers. We show that this strategy is appropriate in prediction problems where the label is spuriously correlated with an attribute. Under the assumptions of such problems, we discuss the favorable sample complexity of counterfactual data augmentation, compared to importance re-weighting. Pragmatically, we match examples using auxiliary data, based on diff-in-diff methodology, and use a large language model (LLM) to represent a conditional probability of text. Through extensive experimentation on learning caregiver-invariant predictors of clinical diagnoses from medical narratives and on semi-synthetic data, we demonstrate that our method for simulating interventions improves out-of-distribution (OOD) accuracy compared to baseline invariant learning algorithms.
Adversarial Counterfactual Environment Model Learning
Authors: Yang Yu, Zhengmao Zhu, ZhiHua Yu, Chen Zhenjun, Chenghe Wang, Yinan Wu, Rong-Jun Qin, Hongqiu Wu, Ruijin Ding, Huang Fangsheng Xiong-Hui Chen
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: An accurate environment dynamics model is crucial for various downstream tasks in sequential decision-making, such as counterfactual prediction, off-policy evaluation, and offline reinforcement learning. Currently, these models were learned through empirical risk minimization (ERM) by step-wise fitting of historical transition data. This way was previously believed unreliable over long-horizon rollouts because of the compounding errors, which can lead to uncontrollable inaccuracies in predictions. In this paper, we find that the challenge extends beyond just long-term prediction errors: we reveal that even when planning with one step, learned dynamics models can also perform poorly due to the selection bias of behavior policies during data collection. This issue will significantly mislead the policy optimization process even in identifying single-step optimal actions, further leading to a greater risk in sequential decision-making scenarios.To tackle this problem, we introduce a novel model-learning objective called adversarial weighted empirical risk minimization (AWRM).  AWRM incorporates an adversarial policy that exploits the model to generate a data distribution that weakens the model's prediction accuracy, and subsequently, the model is learned under this adversarial data distribution.We implement a practical algorithm, GALILEO, for AWRM and evaluate it on two synthetic tasks, three continuous-control tasks, and  \textit{a real-world application}. The experiments demonstrate that GALILEO can accurately predict counterfactual actions and improve various downstream tasks, including offline policy evaluation and improvement, as well as online decision-making.
Finding Safe Zones of Markov Decision Processes Policies
Authors: Yishay Mansour, Michal Moshkovitz Lee Cohen
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Given a policy of a Markov Decision Process, we define a SafeZone as a subset of states, such that most of the policy's trajectories are confined to this subset. The quality of a SafeZone is parameterized by the number of states and the escape probability, i.e., the probability that a random trajectory will leave the subset. SafeZones are especially interesting when they have a small number of states and low escape probability. We study the complexity of finding optimal SafeZones, and show that in general, the problem is computationally hard. For this reason, we concentrate on finding approximate SafeZones. Our main result is a bi-criteria approximation learning algorithm with a factor of almost $2$  approximation for both the escape probability and \newprob size, using a polynomial size sample complexity.
Zero-One Laws of Graph Neural Networks
Authors: Iliant, Ismail Ceylan Sam Adam-Day
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Graph neural networks (GNNs) are the de facto standard deep learning architectures for machine learning on graphs.  This has led to a large body of work analyzing the capabilities and limitations of these models,  particularly pertaining to their representation and extrapolation capacity.  We offer a novel theoretical perspective on the representation and extrapolation capacity of GNNs, by answering the question: how do GNNs behave as the number of graph nodes become very large? Under mild assumptions, we show that when we draw graphs of increasing size from the Erdős–Rényi model, the probability that such graphs are mapped to a particular output by a class of GNN classifiers tends to either zero or one. This class includes the popular graph convolutional network architecture. The result establishes `zero-one laws' for these GNNs, and analogously to other convergence laws,  entails theoretical limitations on their capacity.  We empirically verify our results, observing that the theoretical asymptotic limits are evident already on relatively small graphs.
Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective
Authors: Bohang Zhang, Yuntian Gu, Haotian Ye, Di He, Liwei Wang Guhao Feng
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Recent studies have discovered that Chain-of-Thought prompting (CoT) can dramatically improve the performance of Large Language Models (LLMs), particularly when dealing with complex tasks involving mathematics or reasoning. Despite the enormous empirical success, the underlying mechanisms behind CoT and how it unlocks the potential of LLMs remain elusive. In this paper, we take a first step towards theoretically answering these questions. Specifically, we examine the expressivity of LLMs with CoT in solving fundamental mathematical and decision-making problems. By using circuit complexity theory, we first give impossibility results showing that bounded-depth Transformers are unable to directly produce correct answers for basic arithmetic/equation tasks unless the model size grows super-polynomially with respect to the input length. In contrast, we then prove by construction that autoregressive Transformers of constant size suffice to solve both tasks by generating CoT derivations using a commonly used math language format. Moreover, we show LLMs with CoT can handle a general class of decision-making problems known as Dynamic Programming, thus justifying their power in tackling complex real-world tasks. Finally, an extensive set of experiments show that, while Transformers always fail to directly predict the answers, they can consistently learn to generate correct solutions step-by-step given sufficient CoT demonstrations.
Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback
Authors: Liang Zheng Jaskirat Singh
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: The field of text-conditioned image generation has made unparalleled progress with the recent advent of latent diffusion models. While revolutionary, as the complexity of given text input increases, the current state of art diffusion models may still fail in generating images that accurately convey the semantics of the given prompt. Furthermore, such misalignments are often left undetected by pretrained multi-modal models such as CLIP.  To address these problems, in this paper, we explore a simple yet effective decompositional approach towards both evaluation and improvement of text-to-image alignment. In particular,  we first introduce a Decompositional-Alignment-Score which given a complex caption decomposes it into a set of disjoint assertions. The alignment of each assertion with generated images is then measured using a VQA model. Finally, alignment scores for different assertions are combined aposteriori to give the final text-to-image alignment score. Experimental analysis reveals that the proposed alignment metric shows a significantly higher correlation with human ratings as opposed to traditional CLIP, BLIP scores. Furthermore, we also find that the assertion level alignment scores also provide useful feedback which can then be used in a simple iterative procedure to gradually increase the expressivity of different assertions in the final image outputs. Human user studies indicate that the proposed approach surpasses previous state-of-the-art by 8.7% in overall text-to-image alignment accuracy.
Distributed Personalized Empirical Risk Minimization
Authors: Mohammad Mahdi Kamani, Pouria Mahdavinia, Mehrdad Mahdavi Yuyang Deng
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: This paper advocates a new paradigm  Personalized Empirical Risk Minimization (PERM) to facilitate learning from heterogeneous  data sources without imposing stringent constraints on computational resources shared by participating devices. In PERM, we aim at  learning  a distinct model for each client by personalizing the aggregation of local empirical losses by effectively estimating the  statistical discrepancy among data distributions, which entails  optimal statistical accuracy for all local distributions and overcomes the data heterogeneity issue.  To learn personalized models at scale,  we propose a distributed algorithm that replaces the standard model averaging with model shuffling to simultaneously optimize PERM objectives for all devices. This also allows to learn distinct model architectures (e.g., neural networks with different number of parameters) for  different clients, thus confining to  underlying  memory and compute resources of individual clients. We rigorously analyze the convergence of proposed algorithm and  conduct experiments  that corroborates the effectiveness of proposed paradigm.
Training-free Diffusion Model Adaptation for Variable-Sized Text-to-Image Synthesis
Authors: Xuli Shen, Bin Li, Xiangyang Xue Zhiyu Jin
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Diffusion models (DMs) have recently gained attention with state-of-the-art performance in text-to-image synthesis. Abiding by the tradition in deep learning, DMs are trained and evaluated on the images with fixed sizes. However, users are demanding for various images with specific sizes and various aspect ratio. This paper focuses on adapting text-to-image diffusion models to handle such variety while maintaining visual fidelity. First we observe that, during the synthesis, lower resolution images suffer from incomplete object portrayal, while higher resolution images exhibit repetitively disordered presentation. Next, we establish a statistical relationship indicating that attention entropy changes with token quantity, suggesting that models aggregate spatial information in proportion to image resolution. The subsequent interpretation on our observations is that objects are incompletely depicted due to limited spatial information for low resolutions, while repetitively disorganized presentation arises from redundant spatial information for high resolutions. From this perspective, we propose a scaling factor to alleviate the change of attention entropy and mitigate the defective pattern observed. Extensive experimental results validate the efficacy of the proposed scaling factor, enabling models to achieve better visual effects, image quality, and text alignment. Notably, these improvements are achieved without additional training or fine-tuning techniques.
Enhancing Sharpness-Aware Optimization Through Variance Suppression
Authors: Georgios Giannakis Bingcong Li
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Sharpness-aware minimization (SAM) has well documented merits in enhancing generalization of deep neural networks, even without sizable data augmentation. Embracing the geometry of the loss function, where neighborhoods of 'flat minima' heighten generalization ability, SAM seeks 'flat valleys' by minimizing the maximum loss caused by an adversary perturbing parameters within the neighborhood.Although critical to account for sharpness of the loss function, such an 'over-friendly adversary' can curtail the outmost level of generalization. The novel approach of this contribution fosters stabilization of adversaries through variance suppression (VaSSO) to avoid such friendliness. VaSSO's provable stability safeguards its numerical improvement over SAM in model-agnostic tasks, including image classification and machine translation. In addition, experiments confirm that VaSSO endows SAM with robustness against high levels of label noise. Code is available at https://github.com/BingcongLi/VaSSO.
Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards
Authors: Yimu Wang, Yuanyu Wan, Jinfeng Yi, Lijun Zhang Bo Xue
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: This paper investigates the problem of generalized linear bandits with heavy-tailed rewards, whose $(1+\epsilon)$-th moment is bounded for some $\epsilon\in (0,1]$. Although there exist methods for generalized linear bandits, most of them focus on bounded or sub-Gaussian rewards and are not well-suited for many real-world scenarios, such as financial markets and web-advertising. To address this issue, we propose two novel algorithms based on truncation and mean of medians. These algorithms achieve an almost optimal regret bound of $\widetilde{O}(dT^{\frac{1}{1+\epsilon}})$, where $d$ is the dimension of contextual information and $T$ is the time horizon. Our truncation-based algorithm supports online learning, distinguishing it from existing truncation-based approaches. Additionally, our mean-of-medians-based algorithm requires only $O(\log T)$ rewards and one estimator per epoch, making it more practical. Moreover, our algorithms improve the regret bounds by a logarithmic factor compared to existing algorithms when $\epsilon=1$. Numerical experimental results confirm the merits of our algorithms.
Multinomial Logistic Regression: Asymptotic Normality on Null Covariates in High-Dimensions
Authors: Pierre C Bellec Kai Tan
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: This paper investigates the asymptotic distribution of the maximum-likelihood estimate (MLE) in multinomial logistic models in the high-dimensional regime where dimension and sample size are of the same order. While classical large-sample theory provides asymptotic normality of the MLE under certain conditions, such classical results are expected to fail in high-dimensions as documented for the binary logistic case in the seminal work of Sur and Candès [2019]. We address this issue in classification problems with 3 or more classes, by developing asymptotic normality and asymptotic chi-square results for the multinomial logistic MLE (also known as cross-entropy minimizer) on null covariates. Our theory leads to a new methodology to test the significance of a given feature. Extensive simulation studies on synthetic data corroborate these asymptotic results and confirm the validity of proposed p-values for testing the significance of a given feature.
Why think step by step? Reasoning emerges from the locality of experience
Authors: Michael Li, Noah Goodman Ben Prystawski
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Humans have a powerful and mysterious capacity to reason. Working through a set of mental steps enables us to make inferences we would not be capable of making directly even though we get no additional data from the world. Similarly, when large language models generate intermediate steps (a chain of thought) before answering a question, they often produce better answers than they would directly. We investigate why and how chain-of-thought reasoning is useful in language models, testing the hypothesis that reasoning is effective when training data consists of overlapping local clusters of variables that influence each other strongly. These training conditions enable the chaining of accurate local inferences to estimate relationships between variables that were not seen together in training. We prove that there will exist a "reasoning gap", where reasoning through intermediate variables reduces bias, for the simple case of an autoregressive density estimator trained on local samples from a chain-structured probabilistic model. We then test our hypothesis experimentally in more complex models, training an autoregressive language model on samples from Bayes nets but only including a subset of variables in each sample. We test language models’ ability to match conditional probabilities with and without intermediate reasoning steps, finding that intermediate steps are only helpful when the training data is locally structured with respect to dependencies between variables. The combination of locally structured observations and reasoning is much more data-efficient than training on all variables. Our results illustrate how the effectiveness of reasoning step by step is rooted in the local statistical structure of the training data.
Analyzing Generalization of Neural Networks through Loss Path Kernels
Authors: Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam Nguyen, Lily Weng Yilan Chen
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Deep neural networks have been increasingly used in real-world applications, making it critical to ensure their ability to adapt to new, unseen data. In this paper, we study the generalization capability of neural networks trained with (stochastic) gradient flow. We establish a new connection between the loss dynamics of gradient flow and general kernel machines by proposing a new kernel, called loss path kernel. This kernel measures the similarity between two data points by evaluating the agreement between loss gradients along the path determined by the gradient flow. Based on this connection, we derive a new generalization upper bound that applies to general neural network architectures. This new bound is tight and strongly correlated with the true generalization error. We apply our results to guide the design of neural architecture search (NAS) and demonstrate favorable performance compared with state-of-the-art NAS algorithms through numerical experiments.
Operation-Level Early Stopping for Robustifying Differentiable NAS
Authors: Zipeng Ji, Guanghui Zhu, Chunfeng Yuan, Yihua Huang Shen Jiang
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Differentiable NAS (DARTS) is a simple and efficient neural architecture search method that has been extensively adopted in various machine learning tasks.% Nevertheless, DARTS still encounters several robustness issues, mainly the domination of skip connections.% The resulting architectures are full of parametric-free operations, leading to performance collapse.% Existing methods suggest that the skip connection has additional advantages in optimization compared to other parametric operations and propose to alleviate the domination of skip connections by eliminating these additional advantages.% In this paper, we analyze this issue from a simple and straightforward perspective and propose that the domination of skip connections results from parametric operations overfitting the training data while architecture parameters are trained on the validation data, leading to undesired behaviors.% Based on this observation, we propose the operation-level early stopping (OLES) method to overcome this issue and robustify DARTS without introducing any computation overhead.% Extensive experimental results can verify our hypothesis and the effectiveness of OLES.
Training on Foveated Images Improves Robustness to Adversarial Attacks
Authors: Aqsa Kashaf, Bhiksha Raj Muhammad Shah
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Deep neural networks (DNNs) have been shown to be vulnerable to adversarial attacks-- subtle,  perceptually indistinguishable perturbations of inputs that change the response of the model. In the context of vision, we hypothesize that an important contributor to the robustness of human visual perception is constant exposure to low-fidelity visual stimuli in our peripheral vision. To investigate this hypothesis, we develop RBlur, an image transform that simulates the loss in fidelity of peripheral vision by blurring the image and reducing its color saturation based on the distance from a given fixation point. We show that compared to DNNs trained on the original images, DNNs trained on images transformed by RBlur are substantially more robust to adversarial attacks, as well as other, non-adversarial, corruptions, achieving up to 25% higher accuracy on perturbed data.
Label Poisoning is All You Need
Authors: Jonathan Hayase, Sewoong Oh Rishi Jha
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: In a backdoor attack, an adversary injects corrupted data into a model's training dataset in order to gain control over its predictions on images with a specific attacker-defined trigger. A typical corrupted training example requires altering both the image, by applying the trigger, and the label. Models trained on clean images, therefore, were considered safe from backdoor attacks. However, in some common machine learning scenarios, the training labels are provided by potentially malicious third-parties. This includes crowd-sourced annotation and knowledge distillation. We, hence, investigate a fundamental question: can we launch a successful backdoor attack by only corrupting labels? We introduce a novel approach to design label-only backdoor attacks, which we call FLIP, and demonstrate its strengths on three datasets (CIFAR-10, CIFAR-100, and Tiny-ImageNet) and four architectures (ResNet-32, ResNet-18, VGG-19, and Vision Transformer). With only 2% of CIFAR-10 labels corrupted, FLIP achieves a near-perfect attack success rate of 99.4% while suffering only a 1.8% drop in the clean test accuracy. Our approach builds upon the recent advances in trajectory matching, originally introduced for dataset distillation.
Learning Trajectories are Generalization Indicators
Authors: Zhizheng Zhang, Dacheng Yin, Yan Lu, Nanning Zheng Jingwen Fu
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: This paper explores the connection between learning trajectories of Deep Neural Networks (DNNs) and their generalization capabilities when optimized using (stochastic) gradient descent algorithms. Instead of concentrating solely on the generalization error of the DNN post-training, we present a novel perspective for analyzing generalization error by investigating the contribution of each update step to the change in generalization error. This perspective enable a more direct comprehension of how the learning trajectory influences generalization error. Building upon this analysis, we propose a new generalization bound that incorporates more extensive trajectory information.Our proposed generalization bound depends on the complexity of learning trajectory and the ratio between the bias and diversity of training set. Experimental observations reveal that our method effectively captures the generalization error throughout the training process. Furthermore, our approach can also track changes in generalization error when adjustments are made to learning rates and label noise levels. These results demonstrate that learning trajectory information is a valuable indicator of a model's generalization capabilities.
CoDet: Co-occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection
Authors: Yi Jiang, Xin Wen, Zehuan Yuan, Xiaojuan Qi Chuofan Ma
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Deriving reliable region-word alignment from image-text pairs is critical to learnobject-level vision-language representations for open-vocabulary object detection.Existing methods typically rely on pre-trained or self-trained vision-languagemodels for alignment, which are prone to limitations in localization accuracy orgeneralization capabilities. In this paper, we propose CoDet, a novel approachthat overcomes the reliance on pre-aligned vision-language space by reformulatingregion-word alignment as a co-occurring object discovery problem. Intuitively, bygrouping images that mention a shared concept in their captions, objects corresponding to the shared concept shall exhibit high co-occurrence among the group.CoDet then leverages visual similarities to discover the co-occurring objects andalign them with the shared concept. Extensive experiments demonstrate that CoDethas superior performances and compelling scalability in open-vocabulary detection,e.g., by scaling up the visual backbone, CoDet achieves 37.0 $AP^m_{novel}$ and 44.7 $AP^m_{all}$ on OV-LVIS, surpassing the previous SoTA by 4.2 $AP^m_{novel}$ and 9.8 $AP^m_{all}$. Code is available at https://github.com/CVMI-Lab/CoDet.
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
Authors: Guillaume Couairon, Corentin Dancette, Jean-Baptiste Gaya, Mustafa Shukor, Laure Soulier, Matthieu Cord Alexandre Rame
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further align the network with the intended usage. Yet the imperfections in the proxy reward may hinder the training and lead to suboptimal results; the diversity of objectives in real-world tasks and human opinions exacerbate the issue. This paper proposes embracing the heterogeneity of diverse rewards by following a multi-policy strategy. Rather than focusing on a single a priori reward, we aim for Pareto-optimal generalization across the entire space of preferences. To this end, we propose rewarded soup, first specializing multiple networks independently (one for each proxy reward) and then interpolating their weights linearly. This succeeds empirically because we show that the weights remain linearly connected when fine-tuned on diverse rewards from a shared pre-trained initialization. We demonstrate the effectiveness of our approach for text-to-text (summarization, Q&A, helpful assistant, review), text-image (image captioning, text-to-image generation, visual grounding), and control (locomotion) tasks. We hope to enhance the alignment of deep models, and how they interact with the world in all its diversity.
Optimal Block-wise Asymmetric Graph Construction for Graph-based Semi-supervised Learning
Authors: Yifei Zhang, Irwin King Zixing Song
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Graph-based semi-supervised learning (GSSL) serves as a powerful tool to model the underlying manifold structures of samples in high-dimensional spaces. It involves two phases: constructing an affinity graph from available data and inferring labels for unlabeled nodes on this graph. While numerous algorithms have been developed for label inference, the crucial graph construction phase has received comparatively less attention, despite its significant influence on the subsequent phase. In this paper, we present an optimal asymmetric graph structure for the label inference phase with theoretical motivations. Unlike existing graph construction methods, we differentiate the distinct roles that labeled nodes and unlabeled nodes could play. Accordingly, we design an efficient block-wise graph learning algorithm with a global convergence guarantee. Other benefits induced by our method, such as enhanced robustness to noisy node features, are explored as well. Finally, we perform extensive experiments on synthetic and real-world datasets to demonstrate its superiority to the state-of-the-art graph construction methods in GSSL.
On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence
Authors: Marc Jourdan, Aymen Al Marjani, Debabrota Basu Achraf Azize
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Best Arm Identification (BAI) problems are progressively used for data-sensitive applications, such as designing adaptive clinical trials, tuning hyper-parameters, and conducting user studies to name a few. Motivated by the data privacy concerns invoked by these applications, we study the problem of BAI with fixed confidence under $\epsilon$-global Differential Privacy (DP). First, to quantify the cost of privacy, we derive a lower bound on the sample complexity of any $\delta$-correct BAI algorithm satisfying $\epsilon$-global DP. Our lower bound suggests the existence of two privacy regimes depending on the privacy budget $\epsilon$. In the high-privacy regime (small $\epsilon$), the hardness depends on a coupled effect of privacy and a novel information-theoretic quantity, called the Total Variation Characteristic Time. In the low-privacy regime (large $\epsilon$), the sample complexity lower bound reduces to the classical non-private lower bound. Second, we propose AdaP-TT, an $\epsilon$-global DP variant of the Top Two algorithm. AdaP-TT runs in *arm-dependent adaptive episodes* and adds *Laplace noise* to ensure a good privacy-utility trade-off. We derive an asymptotic upper bound on the sample complexity of AdaP-TT that matches with the lower bound up to multiplicative constants in the high-privacy regime. Finally, we provide an experimental analysis of AdaP-TT that validates our theoretical results.
COCO-Counterfactuals: Automatically Constructed Counterfactual Examples for Image-Text Pairs
Authors: VASUDEV LAL, Phillip Howard Tiep Le
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Datasets and Benchmarks Track), 2023

Abstract: Counterfactual examples have proven to be valuable in the field of natural language processing (NLP) for both evaluating and improving the robustness of language models to spurious correlations in datasets. Despite their demonstrated utility for NLP, multimodal counterfactual examples have been relatively unexplored due to the difficulty of creating paired image-text data with minimal counterfactual changes. To address this challenge, we introduce a scalable framework for automatic generation of counterfactual examples using text-to-image diffusion models. We use our framework to create COCO-Counterfactuals, a multimodal counterfactual dataset of paired image and text captions based on the MS-COCO dataset. We validate the quality of COCO-Counterfactuals through human evaluations and show that existing multimodal models are challenged by our counterfactual image-text pairs. Additionally, we demonstrate the usefulness of COCO-Counterfactuals for improving out-of-domain generalization of multimodal vision-language models via training data augmentation. We make our code and the COCO-Counterfactuals dataset publicly available.
BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis
Authors: Hang Yu, Shizhan Liu, Jianguo Li, Weiyao Lin Zelin Ni
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Bases have become an integral part of modern deep learning-based models for time series forecasting due to their ability to act as feature extractors or future references. To be effective, a basis must be tailored to the specific set of time series data and exhibit distinct correlation with each time series within the set. However, current state-of-the-art methods are limited in their ability to satisfy both of these requirements simultaneously. To address this challenge, we propose BasisFormer, an end-to-end time series forecasting architecture that leverages learnable and interpretable bases. This architecture comprises three components: First, we acquire bases through adaptive self-supervised learning, which treats the historical and future sections of the time series as two distinct views and employs contrastive learning. Next, we design a Coef module that calculates the similarity coefficients between the time series and bases in the historical view via bidirectional cross-attention. Finally, we present a Forecast module that selects and consolidates the bases in the future view based on the similarity coefficients, resulting in accurate future predictions. Through extensive experiments on six datasets, we demonstrate that BasisFormer outperforms previous state-of-the-art methods by 11.04% and 15.78% respectively for univariate and multivariate forecasting tasks. Code isavailable at: https://github.com/nzl5116190/Basisformer.
Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior
Authors: Peter Harrington, Kurt Keutzer, Wahid Bhimji, Dmitriy Morozov, Michael W. Mahoney, Amir Gholami Shashank Subramanian
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Pre-trained machine learning (ML) models have shown great performance for awide range of applications, in particular in natural language processing (NLP)and computer vision (CV). Here, we study how pre-training could be used forscientific machine learning (SciML) applications, specifically in the context oftransfer learning. We study the transfer behavior of these models as (i) the pretrainedmodel size is scaled, (ii) the downstream training dataset size is scaled,(iii) the physics parameters are systematically pushed out of distribution, and (iv)how a single model pre-trained on a mixture of different physics problems canbe adapted to various downstream applications. We find that—when fine-tunedappropriately—transfer learning can help reach desired accuracy levels with ordersof magnitude fewer downstream examples (across different tasks that can even beout-of-distribution) than training from scratch, with consistent behaviour across awide range of downstream examples. We also find that fine-tuning these modelsyields more performance gains as model size increases, compared to training fromscratch on new downstream tasks. These results hold for a broad range of PDElearning tasks. All in all, our results demonstrate the potential of the “pre-train andfine-tune” paradigm for SciML problems, demonstrating a path towards buildingSciML foundation models. Our code is available as open-source.