GenAI4ELab/papercli-papers huggingface dataset:
Why adversarial training can hurt robust accuracy
Authors: Jacob Clarysse; Julia Hörrmann; Fanny Yang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Machine learning classifiers with high test accuracy often perform poorly under adversarial attacks. It is commonly believed that adversarial training alleviates this issue. In this paper, we demonstrate that, surprisingly, the opposite can be true for a natural class of perceptible perturbations --- even though adversarial training helps when enough data is  available, it may in fact hurt robust generalization in the small sample size regime. We first prove this phenomenon for a high-dimensional linear classification setting with noiseless observations. Using intuitive insights from the proof, we could surprisingly find perturbations on standard image datasets for which this behavior persists. Specifically, it occurs for perceptible attacks that effectively reduce class information such as object occlusions or corruptions.
GLM-130B: An Open Bilingual Pre-trained Model
Authors: Aohan Zeng; Xiao Liu; Zhengxiao Du; Zihan Wang; Hanyu Lai; Ming Ding; Zhuoyi Yang; Yifan Xu; Wendi Zheng; Xiao Xia; Weng Lam Tam; Zixuan Ma; Yufei Xue; Jidong Zhai; Wenguang Chen; Zhiyuan Liu; Peng Zhang; Yuxiao Dong; Jie Tang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters. It is an attempt to open-source a 100B-scale model as good as GPT-3 (davinci) and unveil how models of such a scale can be successfully pre-trained. Over the course of this effort, we face numerous unexpected technical and engineering challenges, particularly on loss spikes and divergence. In this paper, we introduce the pre-training process of GLM-130B including its design choices, training strategies for both efficiency and stability, and engineering efforts. The resultant GLM-130B model offers significant outperformance over GPT-3 175B on a wide range of popular English benchmarks while the performance advantage is not observed in OPT-175B and BLOOM-176B. It also consistently and significantly outperforms ERNIE TITAN 3.0 260B—the largest Chinese language model—across related benchmarks. Finally, we leverage a unique scaling property of GLM-130B to reach INT4 quantization with almost no performance loss, making it the first among 100B-scale models and more importantly, allowing its effective inference on 4×RTX 3090 (24G) or 8×RTX 2080 Ti (11G) GPUs, the most ever affordable GPUs required for using 100B-scale models. The GLM-130B model weights are publicly accessible and its code, training logs, related toolkit, and lessons learned are open-sourced at https://github.com/THUDM/GLM-130B/.
The Dynamic of Consensus in Deep Networks and the Identification of Noisy Labels
Authors: Daniel Shwartz; Uri Stern; Daphna Weinshall
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Deep neural networks have incredible capacity and expressibility, and can seemingly memorize any training set. This introduces a problem when training in the presence of noisy labels, as the noisy examples cannot be distinguished from clean examples by the end of training. Recent research has dealt with this challenge by utilizing the fact that deep networks seem to memorize clean examples much earlier than noisy examples. Here we report a new empirical result: for each example, when looking at the time it has been memorized by each model in an ensemble of networks, the diversity seen in noisy examples is much larger than the clean examples. We use this observation to develop a new method for noisy labels filtration. The method is based on a statistics of the data, which captures the differences in ensemble learning dynamics between clean and noisy data. We test our method on three tasks: (i) noise amount estimation; (ii) noise filtration; (iii) supervised classification. We show that our method improves over existing baselines in all three tasks using a variety of datasets, noise models, and noise levels. Aside from its improved performance, our method has two other advantages. (i) Simplicity, which implies that no additional hyperparameters are introduced. (ii) Our method is modular: it does not work in an end-to-end fashion, and can therefore be used to clean a dataset for any other future usage.
Martingale Posterior Neural Processes
Authors: Hyungi Lee; Eunggu Yun; Giung Nam; Edwin Fong; Juho Lee
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: A Neural Process (NP) estimates a stochastic process implicitly defined with neural networks given a stream of data, rather than pre-specifying priors already known, such as Gaussian processes. An ideal NP would learn everything from data without any inductive biases, but in practice, we often restrict the class of stochastic processes for the ease of estimation. One such restriction is the use of a finite-dimensional latent variable accounting for the uncertainty in the functions drawn from NPs. Some recent works show that this can be improved with more “data-driven” source of uncertainty such as bootstrapping. In this work, we take a different approach based on the martingale posterior, a recently developed alternative to Bayesian inference. For the martingale posterior, instead of specifying prior-likelihood pairs, a predictive distribution for future data is specified. Under specific conditions on the predictive distribution, it can be shown that the uncertainty in the generated future data actually corresponds to the uncertainty of the implicitly defined Bayesian posteriors. Based on this result, instead of assuming any form of the latent variables, we equip a NP with a predictive distribution implicitly defined with neural networks and use the corresponding martingale posteriors as the source of uncertainty. The resulting model, which we name as Martingale Posterior Neural Process (MPNP), is demonstrated to outperform baselines on various tasks.
MCTransformer: Combining Transformers And Monte-Carlo Tree Search For Offline Reinforcement Learning
Authors: Gur Yaari; Lior Rokach; Rami Puzis; Gilad Katz
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recent studies explored the framing of reinforcement learning as a sequence modeling problem, and then using Transformers to generate effective solutions. In this study, we introduce MCTransformer, a framework that combines Monte-Carlo Tree Search (MCTS) with Transformers. Our approach uses an actor-critic setup, where the MCTS component is responsible for navigating previously-explored states, aided by input from the Transformer. The Transformer controls the exploration and evaluation of new states, enabling an effective and efficient evaluation of various strategies. In addition to the development of highly effective strategies, our setup enables the use of more efficient sampling compared to existing MCTS-based solutions. MCTransformer is therefore able to perform a small number of evaluations for each newly-explored node, and to do so without degrading its performance. Our evaluation, conducted on the challenging and well-known problem of SameGame, shows that our approach outperforms both Transformer-based and MCTS-based solutions.
Memory of Unimaginable Outcomes in Experience Replay
Authors: Adrian Remonda; Cole Corbitt Terrell; Eduardo E. Veas
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Model-based reinforcement learning (MBRL) applies a single-shot dynamics model to imagined actions to select those with best expected outcome. The dynamics model is an unfaithful representation of the environment physics, and its capacity to predict the outcome of a future action varies as it is trained iteratively. An experience replay buffer collects the outcomes of all actions executed in the environment and is used to iteratively train the dynamics model. With growing experience, it is expected that the model becomes more accurate at predicting the outcome and expected reward of imagined actions. However, training times and memory requirements drastically increase with the growing collection of experiences.  
Indeed, it would be preferable to retain only those experiences that could not be anticipated by the model while interacting with the environment.  
We argue that doing so results in a lean replay buffer with diverse experiences that correspond directly to the model's predictive weaknesses at a given point in time. 
We propose strategies for: i) determining reliable predictions of the dynamics model with respect to the imagined actions, ii) retaining only the unimaginable experiences in the replay buffer, and iii) training further only when sufficient novel experience has been acquired. 
We show that these contributions lead to lower training times, drastic reduction of the replay buffer size, fewer updates to the dynamics model and reduction of catastrophic forgetting. All of which enable the effective implementation of continual-learning agents using MBRL.
NTK-SAP: Improving neural network pruning by aligning training dynamics
Authors: Yite Wang; Dawei Li; Ruoyu Sun
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Pruning neural networks before training has received increasing interest due to its potential to reduce training time and memory. One popular method is to prune the connections based on a  certain metric, but it is not entirely clear what metric is the best choice. Recent advances in neural tangent kernel (NTK) theory suggest that the training dynamics of large enough neural networks is closely related to the spectrum of the NTK. Motivated by this finding, we propose to prune the connections that have the least influence on the spectrum of the NTK. This method can help maintain the NTK spectrum, which may help align the training dynamics to that of its dense counterpart. However, one possible issue is that the fixed-weight-NTK corresponding to a given initial point can be very different from the NTK corresponding to later iterates during the training phase. We further propose to sample multiple realizations of random weights to estimate the NTK spectrum. Note that our approach is weight-agnostic, which is different from most existing methods that are weight-dependent. In addition, we use random inputs to compute the fixed-weight-NTK, making our method data-agnostic as well. We name our foresight pruning algorithm Neural Tangent Kernel Spectrum-Aware Pruning (NTK-SAP). Empirically, our method achieves better performance than all baselines on multiple datasets.
Communication-Efficient and Drift-Robust Federated Learning via Elastic Net
Authors: Seonhyeong Kim; Jiheon Woo; Daewon Seo; Yongjune Kim
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Federated learning (FL) is a distributed method to train a global model over a set of local clients while keeping data localized, which reduces risks of privacy and security. FL framework faces important challenges including expensive communication cost and client drift problem. Leveraging the elastic net, we propose a communication-efficient and drift-robust FL framework to improve the communication efficiency and resolve the client drift problem. We repurpose two types of the elastic net regularizers (i.e., $\ell_1$ and $\ell_2$ penalties on the local model updates): (1) the $\ell_1$-norm regularizer sparsifies the local updates to enhance the communication efficiency and (2) the $\ell_2$-norm regularizer attempts to resolve the client drift problem by limiting the impact of drifting local updates due to data heterogeneity. Our framework is general; hence, it can be integrated with prior FL techniques, e.g., FedAvg, FedProx, SCAFFOLD, and FedDyn. We show that our framework effectively resolves the communication cost problem and the client drift problem simultaneously.
Towards Understanding Robust Memorization in Adversarial Training
Authors: Binghui Li; Yuanzhi Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Adversarial training is a standard method to train neural networks to be robust to adversarial perturbation. However, in contrast with benign overfitting in the standard deep learning setting, which means that over-parameterized neural networks surprisingly generalize well for unseen data, while adversarial training method is able to achieve low robust training error, there still exists a significant robust generalization gap, which promotes us exploring what mechanism leads to robust overfitting during learning process. In this paper, we propose an implicit bias called $\textit{robust memorization}$ in adversarial training under the realistic data assumption. By function approximation theory, we prove that ReLU nets with efficient size have the ability to achieve robust memorization, while robust generalization requires exponentially large models. Then, we demonstrate robust memorization in adversarial training from both empirical and theoretical perspectives. In particular, we empirically investigate the dynamics of loss landscape over input, and we also provide theoretical analysis of robust memorization on data with linear separable assumption. Finally, we prove novel generalization bounds based on robust memorization, which further explains why deep neural networks have both high clean test accuracy and robust overfitting at the same time.
Identifying Phase Transition Thresholds of Permuted Linear Regression via Message Passing
Authors: Hang Zhang; Ping Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: This paper considers the permuted linear regression, i.e., ${\mathbf{Y}} = {\mathbf{\Pi}}^{\natural}{\mathbf{X}}{\mathbf{B}}^{\natural} + {\mathbf{W}}$, where ${\mathbf{Y}} \in \mathbb{R}^{n\times m}, {\mathbf{\Pi}}^{\natural}\in\mathbb{R}^{n\times n}, {\mathbf{X}} \in \mathbb{R}^{n\times p}, {\mathbf{B}}^{\natural}\in \mathbb{R}^{p\times m}$, and ${\mathbf{W}}\in \mathbb{R}^{n\times m}$ represent the observations, missing (or incomplete) information about ordering, sensing matrix, signal of interests, and additive sensing noise, respectively. As is shown in the previous work, there exists phase transition phenomena in terms of the \emph{signal-to-noise ratio} ($\mathsf{snr}$), number of permuted rows, etc. While all existing works only concern the convergence rates without specifying the associate constants in front of them, we give a precise identification of the phase transition thresholds via the message passing algorithm. Depending on whether the signal ${\mathbf{B}}^{\natural}$ is known or not, we separately identify the corresponding critical points around the phase transition regimes. Moreover, we provide numerical experiments and show the empirical phase transition points are well aligned with theoretical predictions.
BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection
Authors: Zehui Chen; Zhenyu Li; Shiquan Zhang; Liangji Fang; Qinhong Jiang; Feng Zhao
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: 3D object detection from multiple image views is a fundamental and challenging task for visual scene understanding. Owing to its low cost and high efficiency, multi-view 3D object detection has demonstrated promising application prospects. However, accurately detecting objects through perspective views is extremely difficult due to the lack of depth information. Current approaches tend to adopt heavy backbones for image encoders, making them inapplicable for real-world deployment. Different from the images, LiDAR points are superior in providing spatial cues, resulting in highly precise localization. In this paper, we explore the incorporation of LiDAR-based detectors for multi-view 3D object detection. Instead of directly training a depth prediction network, we unify the image and LiDAR features in the Bird-Eye-View (BEV) space and adaptively transfer knowledge across non-homogenous representations in a teacher-student paradigm. To this end, we propose BEVDistill, a cross-modal BEV knowledge distillation (KD) framework for multi-view 3D object detection. 
Extensive experiments demonstrate that the proposed method outperforms current KD approaches on a highly-competitive baseline, BEVFormer, without introducing any extra cost in the inference phase. Notably, our best model achieves 59.4 NDS on the nuScenes test leaderboard, achieving new state-of-the-arts in comparison with various image-based detectors. Code will be available at https://github.com/zehuichen123/BEVDistill.
Rethinking Deep Spiking Neural Networks: A Multi-Layer Perceptron Approach
Authors: Luziwei Leng; Boyan Li; Ran Cheng; Shuaijie Shen; Kaixuan Zhang; Jianguo Zhang; Jianxing Liao
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: By adopting deep convolution architectures, spiking neural networks (SNNs) have recently achieved competitive performances with their artificial counterparts in image classification, meanwhile with much lower computation cost due to event-driven and sparse activation. However, the multiplication-free inference (MFI) principle makes SNNs incompatible with attention or transformer mechanisms which have shown significant performance gains on high resolution vision tasks. Inspired from recent works on multi-layer perceptrons (MLPs), we explore an efficient spiking MLP design using batch normalization instead of layer normalization in both the token and the channel block to be compatible with MFI. We further strengthen the network’s local feature learning ability with a spiking patch encoding layer, which significantly improves the network performance. Based on these building blocks, we explore an optimal skip connection configuration and develop an efficient multi-stage spiking MLP network combining global receptive field and local feature extraction,  achieving full spike-based computation. Without pre-training or other advanced SNN training techniques, the spiking MLP network achieves 66.39% top-1 accuracy on the ImageNet-1K dataset, surpassing the state-of-the-art directly trained spiking ResNet-34 by 2.67% under similar model capacity meanwhile with shorter simulation steps and much less computation cost. Another larger variant of the network achieves 68.84% top-1 accuracy, rivaling the spiking VGG-16 network with 4 times smaller model capacity. Our work demonstrates the effectiveness of an alternative deep SNN architecture combining both global and local learning abilities. More interestingly, finally we show a close resemblance of the trained receptive field of our network to cells in the cortex. Code will be publicly available.
Probing into Overfitting for Video Recognition
Authors: Yitian Zhang; Yue Bai; Huan Wang; Yizhou Wang; Yun Fu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Video recognition methods based on 2D networks have thrived in recent years, leveraging advanced image classification techniques. However, overfitting is an even severe problem in 2D video recognition models as 1) the scale of video datasets is relatively small compared to image recognition datasets like ImageNet; 2) current pipeline treats background and semantic frames equally during optimization which aggravates overfitting. Based on these challenges, we design a video-specific data augmentation approach, named as Ghost Motion (GM), to alleviate overfitting. Specifically, GM shifts channels along temporal dimension to enable semantic motion information diffused into other frames which may be irrelevant originally, leading to improvement in frame-wise accuracy. In addition, for challenging video samples with significant temporal dependency (e.g., Something-Something), we further scale the logits during training to prevent overconfident predictions on background frames. Comprehensive empirical validation on various popular datasets shows that the proposed method can improve the generalization of existing methods and is compatible to other competing data augmentation approaches.
TabR: Tabular Deep Learning Meets Nearest Neighbors
Authors: Yury Gorishniy; Ivan Rubachev; Nikolay Kartashev; Daniil Shlenskii; Akim Kotelnikov; Artem Babenko
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Deep learning (DL) models for tabular data problems (e.g. classification, regression) are currently receiving increasingly more attention from researchers.
However, despite the recent efforts, the non-DL algorithms based on gradient-boosted decision trees (GBDT) remain a strong go-to solution for these problems.
One of the research directions aimed at improving the position of tabular DL involves designing so-called retrieval-augmented models.
For a target object, such models retrieve other objects (e.g. the nearest neighbors) from the available training data and use their features and labels to make a better prediction.

In this work, we present TabR -- essentially, a feed-forward network with a custom k-Nearest-Neighbors-like component in the middle.
On a set of public benchmarks with datasets up to several million objects, TabR marks a big step forward for tabular DL: it demonstrates the best average performance among tabular DL models, becomes the new state-of-the-art on several datasets, and even outperforms GBDT models on the recently proposed "GBDT-friendly" benchmark (see Figure 1).
Among the important findings and technical details powering TabR, the main ones lie in the attention-like mechanism that is responsible for retrieving the nearest neighbors and extracting valuable signal from them.
In addition to the higher performance, TabR is simple and significantly more efficient compared to prior retrieval-based tabular DL models.
SaNN: Simple Yet Powerful Simplicial-aware Neural Networks
Authors: Sravanthi Gurugubelli; Sundeep Prabhakar Chepuri
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Simplicial neural networks (SNNs) are deep models for higher-order graph representation learning. SNNs learn low-dimensional embeddings of simplices in a simplicial complex by aggregating features of their respective upper, lower, boundary, and coboundary adjacent simplices. The aggregation in SNNs is carried out during training. Since the number of simplices of various orders in a simplicial complex is significantly large, the memory and training-time requirement in SNNs is enormous. In this work, we propose a scalable simplicial-aware neural network (SaNN) model with a constant run-time and memory requirements independent of the size of the simplicial complex and the density of interactions in it. SaNN is based on pre-aggregated simplicial-aware features as inputs to a neural network, so it has a strong simplicial-structural inductive bias. We provide theoretical conditions under which SaNN is provably more powerful than the Weisfeiler-Lehman (WL) graph isomorphism test and as powerful as the simplicial Weisfeiler-Lehman (SWL) test. We also show that SaNN is permutation and orientation equivariant and satisfies simplicial-awareness of the highest order in a simplicial complex. We demonstrate via numerical experiments that despite being computationally economical, the proposed model achieves state-of-the-art performance in predicting trajectories,  simplicial closures, and classifying graphs.