GenAI4ELab/papercli-papers huggingface dataset:
The Final Ascent: When Bigger Models Generalize Worse on Noisy-Labeled Data
Authors: Yihao Xue; Kyle Whitecross; Baharan Mirzasoleiman
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Increasing the size of overparameterized neural networks has been shown to improve their generalization performance. However, real-world datasets often contain a significant fraction of noisy labels, which can drastically harm the performance of the models trained on them. In this work, we study how neural networks' test loss changes with model size when the training set contains noisy labels. We show that under a sufficiently large noise-to-sample size ratio, generalization error eventually increases with model size. First, we provide a theoretical analysis on random feature regression and show that this phenomenon occurs as the variance of the generalization loss experiences a second ascent under large noise-to-sample size ratio. Then, we present extensive empirical evidence confirming that our theoretical results hold for neural networks. Furthermore, we empirically observe that the adverse effect of network size is more pronounced when robust training methods are employed to learn from noisy-labeled data. Our results have important practical implications: First, larger models should be employed with extra care, particularly when trained on smaller dataset or using robust learning methods. Second, a large sample size can alleviate the effect of noisy labels and allow larger models to achieve a superior performance even under noise.
Visual Timing For Sound Source Depth Estimation in the Wild
Authors: Wei Sun; Lili Qiu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Depth estimation enables a wide variety of 3D applications, such as robotics and autonomous driving. Despite significant work on various depth sensors, it is challenging to develop an all-in-one method to meet multiple basic criteria. In this paper, we propose a novel audio-visual learning scheme by integrating semantic features with physical spatial cues to boost monocular depth with only one microphone. Inspired by the flash-to-bang theory, we develop FBDepth, the first passive audio-visual depth estimation framework. It is based on the difference between the time-of-flight (ToF) of the light and the sound. We formulate sound source depth estimation as an audio-visual event localization task for collision events. To approach decimeter-level depth accuracy, we design a coarse-to-fine pipeline to push the temporary localization accuracy from event-level to millisecond-level by aligning audio-visual correspondence and manipulating optical flow. FBDepth feeds the estimated visual timestamp together with the audio clip and object visual features to regress the source depth. We use a mobile phone to collect 3.6K+ video clips with 24 different objects at up to 60m. FBDepth shows superior performance especially at a long range compared to monocular and stereo methods.
CoRTX: Contrastive Framework for Real-time Explanation
Authors: Yu-Neng Chuang; Guanchu Wang; Fan Yang; Quan Zhou; Pushkar Tripathi; Xuanting Cai; Xia Hu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Recent advancements in explainable machine learning provide effective and faithful solutions for interpreting model behaviors. However, many explanation methods encounter efficiency issues, which largely limit their deployments in practical scenarios. Real-time explainer (RTX) frameworks have thus been proposed to accelerate the model explanation process by learning an one-feed-forward explainer. Existing RTX frameworks typically build the explainer under the supervised learning paradigm, which requires large amounts of explanation labels as the ground truth. Considering that accurate explanation labels are usually hard to obtain, due to constrained computational resources and limited human efforts, effective explainer training is still challenging in practice. In this work, we propose a COntrastive Real-Time eXplanation (CoRTX) framework to learn the explanation-oriented representation and relieve the intensive dependence of explainer training on explanation labels. Specifically, we design a synthetic strategy to select positive and negative instances for explanation representation learning. Theoretical analysis show that our selection strategy can benefit the contrastive learning process on explanation tasks. Experimental results on three real-world datasets further demonstrate the efficiency and efficacy of our proposed CoRTX framework.
Training language models to summarize narratives improves brain alignment
Authors: Khai Loong Aw; Mariya Toneva
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Building systems that achieve a deeper understanding of language is one of the central goals of natural language processing (NLP). Towards this goal, recent works have begun to train language models on narrative datasets which require extracting the most critical information by integrating across long contexts. However, it is still an open question whether these models are learning a deeper understanding of the text, or if the models are simply learning a heuristic to complete the task. This work investigates this further by turning to the one language processing system that truly understands complex language: the human brain. We show that training language models for deeper narrative understanding results in richer representations that have improved alignment to human brain activity. We further find that the improvements in brain alignment are larger for character names than for other discourse features, which indicates that these models are learning important narrative elements. Taken together, these results suggest that this type of training can indeed lead to deeper language understanding. These findings have consequences both for cognitive neuroscience by revealing some of the significant factors behind brain-NLP alignment, and for NLP by highlighting that understanding of long-range context can be improved beyond language modeling.
Robust Constrained Reinforcement Learning
Authors: Yue Wang; Fei Miao; Shaofeng Zou
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Constrained reinforcement learning is to maximize the reward subject to constraints on utilities/costs. However, in practice it is often the case  that the training environment is not the same as the test one, due to, e.g., modeling error, adversarial attack, non-stationarity, resulting in severe performance degradation and more importantly constraint violation in the test environment. To address this challenge, we formulate the framework of robust constrained reinforcement learning under model uncertainty, where the MDP is not fixed but lies in some uncertainty set. The goal is two fold: 1) to guarantee that constraints on utilities/costs are satisfied for all MDPs in the uncertainty set, and 2) to maximize the worst-case reward performance over the uncertainty set. We design a robust primal-dual approach, and further develop theoretical guarantee on its convergence, complexity and robust feasibility. We then investigate a concrete example of $\delta$-contamination uncertainty set, design an online and model-free algorithm and theoretically characterize its sample complexity.
Adversarial Policies Beat Professional-Level Go AIs
Authors: Tony Tong Wang; Adam Gleave; Nora Belrose; Tom Tseng; Michael D Dennis; Yawen Duan; Viktor Pogrebniak; Sergey Levine; Stuart Russell
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We attack the state-of-the-art Go-playing AI system, KataGo, by training an adversarial policy that plays against a frozen KataGo victim. Our attack achieves a >99% win-rate against KataGo without search, and a >80% win-rate when KataGo uses enough search to be near-superhuman. To the best of our knowledge, this is the first successful end-to-end attack against a Go AI playing at the level of a top human professional. Notably, the adversary does not win by learning to play Go better than KataGo---in fact, the adversary is easily beaten by human amateurs. Instead, the adversary wins by tricking KataGo into ending the game prematurely at a point that is favorable to the adversary. Our results demonstrate that even professional-level AI systems may harbor surprising failure modes.
Hidden Schema Networks
Authors: Ramses J Sanchez; Lukas Alexander Conrads; Pascal Welke; Kostadin Cvejoski; Cesar Ojeda
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Most modern language models infer representations that, albeit powerful, lack both compositionality and semantic interpretability. Starting from the assumption that a large proportion of semantic content is necessarily relational, we introduce a neural language model that discovers networks of symbols (schemata) from text datasets. Using a variational autoencoder (VAE) framework, our model encodes sentences into sequences of symbols (composed representation), which correspond to the nodes visited by biased random walkers on a global latent graph. We first demonstrate that the model is able to uncover ground-truth graphs from artificially generated datasets of random token sequences. Next we leverage pretrained BERT and GPT-2 language models as encoder and decoder, respectively, to train our model on language modelling and commonsense knowledge generation tasks. Qualitatively, the model is able to infer schema networks whose nodes (symbols) can be interpreted as encoding different aspects of natural language (as e.g. topics, sentiments). Quantitatively, our results show that the model successfully interprets the encoded symbol sequences, as it achieves state-of-the-art scores on VAE language modeling benchmarks. Source code to reproduce all experiments is provided with the supplementary material.
Efficient Certified Training and Robustness Verification of Neural ODEs
Authors: Mustafa Zeqiri; Mark Niklas Mueller; Marc Fischer; Martin Vechev
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Neural Ordinary Differential Equations (NODEs) are a novel neural architecture, built around initial value problems with learned dynamics which are solved during inference. Thought to be inherently more robust against adversarial perturbations, they were recently shown to be vulnerable to strong adversarial attacks, highlighting the need for formal guarantees.  However, despite significant progress in robustness verification for standard feed-forward architectures, the verification of high dimensional NODEs remains an open problem. In this work we address this challenge and propose GAINS, an analysis framework for NODEs combining three key ideas: (i) a novel class of ODE solvers, based on variable but discrete time steps, (ii) an efficient graph representation of solver trajectories, and (iii) a novel abstraction algorithm operating on this graph representation. Together, these advances enable the efficient analysis and certified training of high-dimensional NODEs, by reducing the runtime from an intractable $\mathcal{O}(\exp(d)+\exp(T))$ to $\mathcal{O}(d+T^2\log^2T)$ in the dimensionality $d$ and integration time $T$.  In an extensive evaluation on computer vision (MNIST and Fashion-MNIST) and time-series forecasting (Physio-Net) problems, we demonstrate the effectiveness of both our certified training and verification methods.
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
Authors: Yi-Lun Liao; Tess Smidt
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Despite their widespread success in various domains, Transformer networks have yet to perform well across datasets in the domain of 3D atomistic graphs such as molecules even when 3D-related inductive biases like translational invariance and rotational equivariance are considered. In this paper, we demonstrate that Transformers can generalize well to 3D atomistic graphs and present Equiformer, a graph neural network leveraging the strength of Transformer architectures and incorporating SE(3)/E(3)-equivariant features based on irreducible representations (irreps). First, we propose a simple and effective architecture by only replacing original operations in Transformers with their equivariant counterparts and including tensor products. Using equivariant operations enables encoding equivariant information in channels of irreps features without complicating graph structures. With minimal modifications to Transformers, this architecture has already achieved strong empirical results. Second, we propose a novel attention mechanism called equivariant graph attention, which improves upon typical attention in Transformers through replacing dot product attention with multi-layer perceptron attention and including non-linear message passing. With these two innovations, Equiformer achieves competitive results to previous models on QM9, MD17 and OC20 datasets.
Discrete State-Action Abstraction via the Successor Representation
Authors: Amnon Attali; Pedro Cisneros-Velarde; Marco Morales; Nancy Amato
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: While the difficulty of reinforcement learning problems is typically related to the complexity of their state spaces, Abstraction proposes that solutions often lie in simpler underlying latent spaces. Prior works have focused on learning either a continuous or dense abstraction, or require a human to provide one. Information-dense representations capture features irrelevant for solving tasks, and continuous spaces can struggle to represent discrete objects. In this work we automatically learn a sparse discrete abstraction of the underlying environment. We do so using a simple end-to-end trainable model based on the successor representation and max-entropy regularization. We describe an algorithm to apply our model, named Discrete State-Action Abstraction (DSAA), which computes an action abstraction in the form of temporally extended actions, i.e., Options, to transition between discrete abstract states. Empirically, we demonstrate the effects of different exploration schemes on our resulting abstraction, and show that it is efficient for solving downstream tasks.
Test-time recalibration of conformal predictors under distribution shift based on unlabeled examples
Authors: Fatih Furkan Yilmaz; Reinhard Heckel
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Modern image classifiers achieve high predictive accuracy, but the predictions typically come without reliable uncertainty estimates. Conformal prediction algorithms provide uncertainty estimates by predicting a set of classes based on the probability estimates of the classifier (for example, the softmax scores). To provide such sets, conformal prediction algorithms often rely on estimating a cutoff threshold for the probability estimates, and this threshold is chosen based on a calibration set. Conformal prediction methods guarantee reliability only when the calibration set is from the same distribution as the test set. Therefore, the methods need to be recalibrated for new distributions. However, in practice, labeled data from new distributions is rarely available, making calibration infeasible. In this work, we consider the problem of predicting the cutoff threshold for a new distribution only based on unlabeled examples. While it is impossible in general to guarantee reliability when calibrating based on unlabeled examples, we show that our method provides excellent uncertainty estimates under natural distribution shifts.
Reparameterization through Spatial Gradient Scaling
Authors: Alexander Detkov; Mohammad Salameh; Muhammad Fetrat; Jialin Zhang; Robin Luwei; SHANGLING JUI; Di Niu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Reparameterization aims to improve the generalization of deep neural networks by transforming a convolution operation into equivalent multi-branched structures during training. However, there exists a gap in understanding how reparameterization may change and benefit learning processes for neural networks. In this paper, we present a novel spatial gradient scaling method to redistribute learning focus among weights in convolutional neural networks. We prove that spatial gradient scaling achieves the same learning dynamics as a branched reparameterization yet without introducing structural changes into the network. We further propose an analytical approach that dynamically learns scalings for each convolutional layer based on the spatial characteristics of its input feature map gauged by mutual information. Experiments on CIFAR-10, CIFAR-100, and ImageNet show that without searching for reparameterized structures, our proposed scaling method outperforms the state-of-the-art reparameterization methods at a lower computational cost.
Learning Globally Smooth Functions on Manifolds
Authors: Juan Cervino; Luiz F. O. Chamon; Benjamin David Haeffele; Rene Vidal; Alejandro Ribeiro
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Smoothness and low dimensional structures play central roles in improving generalization and stability in learning and statistics. The combination of these properties has led to many advances in semi-supervised learning, generative modeling, and control of dynamical systems. However, learning smooth functions is generally challenging, except in simple cases such as learning linear or kernel models. Typical methods are either too conservative, relying on crude upper bounds such as spectral normalization, too lax, penalizing smoothness on average, or too computationally intensive, requiring the solution of large-scale semi-definite programs. These issues are only exacerbated when trying to simultaneously exploit low dimensionality using, e.g., manifolds. This work proposes to overcome these obstacles by combining techniques from semi-infinite constrained learning and manifold regularization. To do so, it shows that, under typical conditions, the problem of learning a Lipschitz continuous function on a manifold is equivalent to a dynamically weighted manifold regularization problem. This observation leads to a practical algorithm based on a weighted Laplacian penalty whose weights are adapted using stochastic gradient techniques. We prove that, under mild conditions, this method estimates the Lipschitz constant of the solution, learning a globally smooth solution as a byproduct. Numerical examples illustrate the advantages of using this method to impose global smoothness on manifolds as opposed to imposing smoothness on average.
Interventional Rationalization
Authors: Linan Yue; Qi Liu; Li Wang; Yanqing An; Yichao Du; Zhenya Huang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Selective rationalizations improve the explainability of neural networks by selecting a subsequence of the input (i.e., rationales) to explain the prediction results. Although existing methods have achieved promising results, they still suffer from adopting the spurious correlations in data (aka., shortcuts) to compose rationales and make predictions. Inspired by the causal theory, in this paper, we develop an interventional rationalization (Inter-RAT) to discover the causal rationales. Specifically, we first analyse the causalities among the input, rationales and results with a structural causal model. Then, we discover spurious correlations between input and rationales, and between rationales and results, respectively, by identifying the confounder in the causalities. Next, based on the backdoor adjustment, we propose a causal intervention method to remove the spurious correlations in input and rationales. Further, we discuss reasons why spurious correlations between the selected rationales and results exist by analysing the limitations of the sparsity constraint in the rationalization, and employ the causal intervention method to remove these correlations. Extensive experimental results on three real-world datasets clearly validate the effectiveness of our proposed method.
Federated Semi-supervised Learning with Dual Regulator
Authors: Sikai Bai; Shuaicheng Li; Weiming Zhuang; Kunlin Yang; Jun Hou; Shuai Yi; Shuai Zhang; Junyu Gao; Song Guo
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Federated learning emerges as a powerful method to learn from decentralized heterogeneous data while protecting data privacy. Federated semi-supervised learning (FSSL) is even more practical and challenging, where only a fraction of data can be labeled due to high annotation cost. Existing FSSL methods, however, assume independent and identically distributed (IID) labeled data across clients and consistent class distribution between labeled and unlabeled data within a client. In this work, we propose a novel FSSL framework with dual regulator, FedDure, to optimize and customize model training according to specific data distributions of clients. FedDure lifts the previous assumption with a coarse-grained regulator (C-reg) and a fine-grained regulator (F-reg): C-reg regularizes the updating of local model by tracking the learning effect on labeled data distribution; F-reg learns an adaptive weighting scheme tailored for unlabeled instances in each client. We further formulate the client model training as bi-level optimization that adaptively optimize the model in the client with two regulators. Theoretically, we show the convergence guarantee of dual regulator. Empirically, we demonstrate that FedDure is superior to the existing methods across wide range of settings, notably by more than 12% on CIFAR-10 and CINIC-10 datasets.
DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training
Authors: Joya Chen; Kai Xu; Yuhui Wang; Yifei Cheng; Angela Yao
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: A standard hardware bottleneck when training deep neural networks is GPU memory. The bulk of memory is occupied by caching intermediate tensors for gradient computation in the backward pass. We propose a novel method to reduce this footprint - Dropping Intermediate Tensors (DropIT).  DropIT drops min-k elements of the intermediate tensors and approximates gradients from the sparsified tensors in the backward pass. Theoretically, DropIT reduces noise on estimated gradients and therefore has a higher rate of convergence than vanilla-SGD. Experiments show that we can drop up to 90\% of the intermediate tensor elements in fully-connected and convolutional layers while achieving higher testing accuracy for Visual Transformers and Convolutional Neural Networks on various tasks (e.g., classification, object detection, instance segmentation). Our code and models are available at https://github.com/chenjoya/dropit.
3D Surface Reconstruction in the Wild by Deforming Shape Priors from Synthetic Data
Authors: Nicolai Haeni; Jun-Jee Chao; Volkan Isler
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We present a new method for category-specific 3D reconstruction from a single image. A limitation of current color image-based 3D reconstruction models is that they do not generalize across datasets, due to domain shift. In contrast, we show that one can learn to reconstruct objects across datasets by shape priors learned from synthetic 3D data and a point cloud pose canonicalization method. Given a single depth image at test time, we first place this partial point cloud in a canonical pose. Then, we use a neural deformation field in the canonical coordinate frame to reconstruct the 3D surface of the object. Finally, we jointly optimize object pose and 3D shape to fit the partial depth observation. Our approach achieves state-of-the-art reconstruction performance across several real-world datasets, even when trained without ground truth camera poses (which are required by some of the state-of-the-art methods). We further show that our method generalizes to different input modalities, from dense depth images to sparse and noisy LIDAR scans.