GenAI4ELab/papercli-papers huggingface dataset:
Contrastive Consistent Representation Distillation
Authors: Shipeng Fu; Haoran Yang; Xiaomin Yang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The combination of knowledge distillation with contrastive learning has great potential to distill structural knowledge. Most of the contrastive-learning-based distillation methods treat the entire training dataset as the memory bank and maintain two memory banks, one for the student and one for the teacher. Besides, the representations in the two memory banks are updated in a momentum manner, leading to representation inconsistency. In this work, we propose Contrastive Consistent Representation Distillation (CoCoRD) to provide consistent representations for efficient contrastive-learning-based distillation. Instead of momentum-updating the cached representations, CoCoRD updates the encoders in a momentum manner. Specifically, the teacher is equipped with a momentum-updated projection head to generate consistent representations. These teacher representations are cached in a fixed-size queue which serves as the only memory bank in CoCoRD and is significantly smaller than the entire training dataset. Additionally, a slow-moving student, implemented as a momentum-based moving average of the student, is built to facilitate contrastive learning. CoCoRD, which utilizes only one memory bank and much fewer negative keys, provides highly competitive results under typical teacher-student settings. On ImageNet, CoCoRD-distilled ResNet50 outperforms the teacher ResNet101 by 0.2% top-1 accuracy. Furthermore, in PASCAL VOC and COCO detection, the detectors whose backbones are initialized by CoCoRD-distilled models exhibit considerable performance improvements.
Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling
Authors: Keyu Tian; Yi Jiang; qishuai diao; Chen Lin; Liwei Wang; Zehuan Yuan
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: We identify and overcome two key obstacles in extending the success of BERT-style pre-training, or masked image modeling, to convolutional networks (convnets): (i) convolution operation cannot handle irregular, randomly masked input images; (ii) the single-scale nature of BERT pre-training is inconsistent with convnet’s hierarchical structure. For (i), we treat unmasked pixels as sparse voxels of 3D point clouds and use sparse convolution to encode. This is the first use of sparse convolution for 2D masked modeling. For (ii), we develop a hierarchical decoder to reconstruct images from multi-scale encoded features. Our method, called Sparse masKed modeling (SparK), is general: it can be used directly on any convolutional model without backbone modifications. We validate it on both classical (ResNet) and modern (ConvNeXt) models: on three downstream tasks, it surpasses both state-of-the-art contrastive learning and transformer-based masked modeling by similarly large margins (around +1.0%). The improvements on object detection and instance segmentation are more significant (up to +3.5%), validating the strong transferability of features learned. We also find SparK’s favorable scaling behavior by observing more gains on larger networks. All of these findings support the promising future of generative pre-training on convnets. Both codes and pre-trained models have been released at https://github.com/keyu-tian/SparK.
Multimodal Analogical Reasoning over Knowledge Graphs
Authors: Ningyu Zhang; Lei Li; Xiang Chen; Xiaozhuan Liang; Shumin Deng; Huajun Chen
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Analogical reasoning is fundamental to human cognition and holds an important place in various fields. However, previous studies mainly focus on single-modal analogical reasoning and ignore taking advantage of structure knowledge. Notably, the research in cognitive psychology has demonstrated that information from multimodal sources always brings more powerful cognitive transfer than single modality sources. To this end, we introduce the new task of multimodal analogical reasoning over knowledge graphs, which requires multimodal reasoning ability with the help of background knowledge. Specifically, we construct a Multimodal Analogical Reasoning dataSet (MARS) and a multimodal knowledge graph MarKG. We evaluate with multimodal knowledge graph embedding and pre-trained Transformer baselines, illustrating the potential challenges of the proposed task. We further propose a novel model-agnostic Multimodal analogical reasoning framework with Transformer (MarT) motivated by the structure mapping theory, which can obtain better performance. We hope our work can deliver benefits and inspire future research. Code and datasets are available in https://github.com/zjunlp/MKG_Analogy.
Temporally Consistent Video Transformer for Long-Term Video Prediction
Authors: Wilson Yan; Danijar Hafner; Stephen James; Pieter Abbeel
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Generating long, temporally consistent video remains an open challenge in video generation. Primarily due to computational limitations, most prior methods limit themselves to training on a small subset of frames that are then extended to generate longer videos through a sliding window fashion. Although these techniques may produce sharp videos, they have difficulty retaining long-term temporal consistency due to their limited context length. In this work, we present Temporally Consistent Video Transformer (TECO), a vector-quantized latent dynamics video prediction model that learns compressed representations to efficiently condition on long videos of hundreds of frames during both training and generation. We use a MaskGit prior for dynamics prediction which enables both sharper and faster generations compared to prior work. Our experiments show that TECO outperforms SOTA baselines in a variety of video prediction benchmarks ranging from simple mazes in DMLab, large 3D worlds in Minecraft, and complex real-world videos from Kinetics-600. In addition, to better understand the capabilities of video prediction models in modeling temporal consistency, we introduce several challenging video prediction tasks consisting of agents randomly traversing 3D scenes of varying difficulty. This presents a challenging benchmark for video prediction in partially observable environments where a model must understand what parts of the scenes to re-create versus invent depending on its past observations or generations. An anonymized website with samples can be found at https://sites.google.com/view/iclr23-teco
Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation
Authors: Bobby He; James Martens; Guodong Zhang; Aleksandar Botev; Andrew Brock; Samuel L Smith; Yee Whye Teh
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Skip connections and normalisation layers form two standard architectural components that are ubiquitous for the training of Deep Neural Networks (DNNs), but whose precise roles are poorly understood. Recent approaches such as Deep Kernel Shaping have made progress towards reducing our reliance on them, using insights from wide NN kernel theory to improve signal propagation in vanilla DNNs (which we define as networks without skips or normalisation). However, these approaches are incompatible with the self-attention layers present in transformers, whose kernels are intrinsically more complicated to analyse and control.  And so the question remains: \emph{is it possible to train deep vanilla transformers?} We answer this question in the affirmative by designing several approaches that use combinations of parameter initialisations, bias matrices and location-dependent rescaling to achieve faithful signal propagation in vanilla transformers. Our methods address various intricacies specific to signal propagation in transformers, including the interaction with positional encoding and causal masking. In experiments on WikiText-103 and C4, our approaches enable deep transformers without normalisation to train at speeds matching their standard counterparts, and deep vanilla transformers to reach the same performance as standard ones after about 5 times more iterations.
H2RBox: Horizontal Box Annotation is All You Need for Oriented Object Detection
Authors: Xue Yang; Gefan Zhang; Wentong Li; Yue Zhou; Xuehui Wang; Junchi Yan
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Oriented object detection emerges in many applications from aerial images to autonomous driving, while many existing detection benchmarks are annotated with horizontal bounding box only which is also less costive than fine-grained rotated box, leading to a gap between the readily available training corpus and the rising demand for oriented object detection.  This paper proposes a simple yet effective oriented object detection approach called H2RBox merely using horizontal box annotation for weakly-supervised training, which closes the above gap and shows competitive performance even against those trained with rotated boxes.  The cores of our method are weakly- and self-supervised learning, which predicts the angle of the object by learning the consistency of two different views. To our best knowledge, H2RBox is the first horizontal box annotation-based oriented object detector. Compared to an alternative i.e. horizontal box-supervised instance segmentation with our post adaption to oriented object detection, our approach is not susceptible to the prediction quality of mask and can perform more robustly in complex scenes containing a large number of dense objects and outliers. Experimental results show that H2RBox has significant performance and speed advantages over horizontal box-supervised instance segmentation methods, as well as lower memory requirements. While compared to rotated box-supervised oriented object detectors, our method shows very close performance and speed. The source code is available at PyTorch-based \href{https://github.com/yangxue0827/h2rbox-mmrotate}{MMRotate} and Jittor-based \href{https://github.com/yangxue0827/h2rbox-jittor}{JDet}.
ONLINE RESTLESS BANDITS WITH UNOBSERVED STATES
Authors: Bowen Jiang; Bo Jiang; Jian Li; TAO LIN; Xinbing Wang; Chenghu Zhou
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We study the online restless bandit problem, where each arm evolves according to a Markov chain independently, and the reward of pulling an arm depends on both the current state of the corresponding Markov chain and the action. The agent (decision maker) does not know the transition kernels and reward functions, and cannot observe the states of arms all the time.  The goal is to sequentially choose which arms to pull so as to maximize the expected cumulative rewards collected.  In this paper, we propose TSEETC, a learning algorithm based on Thompson Sampling with Episodic Explore-Then-Commit. The algorithm proceeds in episodes of increasing length and each episode is divided into exploration and exploitation phases. In the exploration phase in each episode, action-reward samples are collected in a round-robin way and then used to update the posterior as a mixture of Dirichlet distributions.  At the beginning of the exploitation phase, TSEETC generates a sample from the posterior distribution as true parameters. It then follows the optimal policy for the sampled model for the rest of the episode. We establish the Bayesian regret bound $\tilde {\mathcal{O}}(\sqrt{T})$ for TSEETC, where $T$ is the time horizon. This is the first bound that is close to the lower bound of restless bandits, especially in an unobserved state setting. We show through simulations that TSEETC outperforms existing algorithms in regret.
NERDS: A General Framework to Train Camera Denoisers from Raw-RGB Noisy Image Pairs
Authors: Heewon Kim; Kyoung Mu Lee
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract:   We aim to train accurate denoising networks for smartphone/digital cameras from single noisy images. Downscaling is commonly used as a practical denoiser for low-resolution images. Based on this processing, we found that the pixel variance of the natural images is more robust to downscaling than the pixel variance of the camera noises. Intuitively, downscaling easily removes high-frequency noises than natural textures. To utilize this property, we can adopt noisy/clean image synthesis at low-resolution to train camera denoisers. On this basis, we propose a new solution pipeline -- NERDS that estimates camera noises and synthesizes noisy-clean image pairs from only noisy images.  In particular, it first models the noise in raw-sensor images as a Poisson-Gaussian distribution, then estimates the noise parameters using the difference of pixel variances by downscaling. We formulate the noise estimation as a gradient-descent-based optimization problem through a reparametrization trick. We further introduce a new Image Signal Processor (ISP) estimation method that enables denoiser training in a human-readable RGB space by transforming the synthetic raw images to the style of a given RGB noisy image. The noise and ISP estimations utilize rich augmentation to synthesize image pairs for denoiser training. Experiments show that our NERDS can accurately train CNN-based denoisers (e.g., DnCNN, ResNet-style network) outperforming previous noise-synthesis-based and self-supervision-based denoisers in real datasets.
Global Counterfactual Explanations Are Reliable Or Efficient, But Not Both
Authors: Dan Ley; Saumitra Mishra; Daniele Magazzeni
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods emerging in fairness, recourse and model understanding. The major shortcoming associated with these methods, however, is their inability to provide explanations beyond the local or instance-level. While many works touch upon the notion of a global explanation, typically suggesting to aggregate masses of local explanations in the hope of ascertaining global properties, few provide frameworks that are both reliable and computationally tractable. Meanwhile, practitioners are requesting more efficient and interactive explainability tools. We take this opportunity to investigate existing methods, improving the efficiency of Actionable Recourse Summaries (AReS), one of the only known global recourse frameworks, and proposing Global & Efficient Counterfactual Explanations (GLOBE-CE), a novel and flexible framework that tackles the scalability issues associated with current state-of-the-art, particularly on higher dimensional datasets and in the presence of continuous features. Furthermore, we provide a unique mathematical analysis of categorical feature translations, utilising it in our method. Experimental evaluation with real world datasets and user studies verify the speed, reliability and interpretability improvements of our framework.
On Convergence of Average-Reward Off-Policy Control Algorithms in Weakly-Communicating MDPs
Authors: Yi Wan; Richard S. Sutton
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We show two average-reward off-policy control algorithms, Differential Q Learning (Wan, Naik, \& Sutton 2021a) and RVI Q Learning (Abounadi Bertsekas \& Borkar 2001), converge in weakly-communicating MDPs. Weakly-communicating MDPs are the most general class of MDPs that a learning algorithm with a single stream of experience can guarantee obtaining a policy achieving optimal reward rate. The original convergence proofs of the two algorithms require that all optimal policies induce unichains, which is not necessarily true for weakly-communicating MDPs. To the best of our knowledge, our results are the first showing average-reward off-policy control algorithms converge in weakly-communicating MDPs. As a direct extension, we show that average-reward options algorithms introduced by (Wan, Naik, \& Sutton 2021b) converge if the Semi-MDP induced by options is weakly-communicating.
Empowering Networks With Scale and Rotation Equivariance Using A Similarity Convolution
Authors: Zikai Sun; Thierry Blu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The translational equivariant nature of Convolutional Neural Networks (CNNs) is a reason for its great success in computer vision. However, networks do not enjoy more general equivariance properties such as rotation or scaling, ultimately limiting their generalization performance. To address this limitation, we devise a method that endows CNNs with simultaneous equivariance with respect to translation, rotation, and scaling.  Our approach defines a convolution-like operation and ensures equivariance based on our proposed scalable Fourier-Argand representation. The method maintains similar efficiency as a traditional network and hardly introduces any additional learnable parameters, since it does not face the computational issue that often occurs in group-convolution operators. We validate the efficacy of our approach in the image classification task, demonstrating its robustness and the generalization ability to both scaled and rotated inputs.
Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models
Authors: Guande He; Jianfei Chen; Jun Zhu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Large pre-trained language models (PLMs) have demonstrated strong performance on natural language understanding (NLU) tasks through fine-tuning. However, fine-tuned models still suffer from overconfident predictions, especially in out-of-domain settings. In this paper, we tackle the problem of calibrating fine-tuned language models. We demonstrate that the PLMs are well-calibrated on the masked language modeling task with robust predictive confidence under domain shift, yet the fine-tuned models fail to retain such property due to catastrophic forgetting, which impacts the calibration on the downstream classification task. In light of these observations, we evaluate the calibration of several methods that preserve pre-trained features and show that preserving pre-trained features can improve the calibration of fine-tuned language models. Among these methods, our proposed method that encourages the fine-tuned model to learn generative representations with auxiliary language modeling objective achieves competitive accuracy and the lowest expected calibration error compared to several strong baselines under both in-domain and out-of-domain settings on three downstream NLU tasks.
A Closer Look at Self-supervised Lightweight Vision Transformers
Authors: Shaoru Wang; Jin Gao; Zeming Li; Weiming Hu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Self-supervised learning on large-scale Vision Transformers (ViTs) as pre-training methods has achieved promising downstream performance. Yet, how much these pre-training paradigms promote lightweight ViTs' performance is considerably less studied. In this work, we mainly develop and benchmark self-supervised pre-training methods, e.g., contrastive-learning-based MoCo-v3, masked-image-modeling-based MAE on image classification tasks, and some downstream dense prediction tasks. We surprisingly find that if proper pre-training is adopted, even vanilla lightweight ViTs show comparable performance on ImageNet to previous SOTA networks with delicate architecture design. We also point out some defects of such pre-training, \eg, failing to benefit from large-scale pre-training data and showing inferior performance on data-insufficient downstream tasks. Furthermore, we analyze and clearly show the effect of such pre-training by analyzing the properties of the layer representation and attention maps for related models. Finally, based on the above analyses, a distillation strategy during pre-training is developed, which leads to further downstream performance improvement for MAE-based pre-training.
Fair Graph Message Passing with Transparency
Authors: Zhimeng Jiang; Xiaotian Han; Chao Fan; Zirui Liu; Na Zou; Ali Mostafavi; Xia Hu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recent advanced works achieve fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs). These methods \textit{implicitly} encode the sensitive attribute information in the well-trained model weight via \textit{backward propagation}. In practice, we not only pursue a fair machine learning model but also lend such fairness perception to the public. For current fairness methods,
how the sensitive attribute information usage makes the model achieve fair prediction still remains a black box. In this work, we first propose the concept \textit{transparency} to describe \textit{whether} the model embraces the ability of lending fairness perception to the public \textit{or not}. Motivated by the fact that current fairness models lack of transparency, we aim to pursue a fair machine learning model with transparency via \textit{explicitly} rendering sensitive attribute usage for fair prediction in \textit{forward propagation} . Specifically, we develop an effective and transparent \textsf{F}air \textsf{M}essage \textsf{P}assing (FMP) scheme adopting sensitive attribute information in forward propagation. In this way, FMP explicitly uncovers how sensitive attributes influence final prediction. Additionally, FMP scheme can aggregate useful information from neighbors and mitigate bias in a unified framework to simultaneously achieve graph smoothness and fairness objectives. An acceleration approach is also adopted to improve the efficiency of FMP. Experiments on node classification tasks demonstrate that the proposed FMP outperforms the state-of-the-art baselines in terms of fairness and accuracy on three real-world datasets. The code is available in {\color{blue}\url{https://anonymous.4open.science/r/FMP-AD84}}.
Solving and Learning non-Markovian Stochastic Control problems in continuous-time with Neural RDEs
Authors: Melker Höglund; Emilio Ferrucci; Camilo Hernández; Aitor Muguruza Gonzalez; Cristopher Salvi; Leandro Sánchez-Betancourt; Yufei Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We propose a novel framework for solving continuous-time, non-Markovian stochastic control problems with the use of neural rough differential equations (Neural RDEs). By parameterising the control process as the solution of a Neural RDE driven by the state process, we show that the control-state joint dynamics are governed by an uncontrolled RDE with structured vector fields, allowing for efficient trajectories simulation, Monte-Carlo estimation of the value function and backpropagation. To deal with input paths of infinite 1-variation, we refine the existing universal approximation result to a probabilistic density result for Neural RDEs driven by random rough paths. Experiments on various non-Markovian problems indicate how the proposed framework is time-resolution-invariant and capable of learning optimal solutions with higher accuracy than traditional RNN-based approaches. Finally, we discuss possible extensions of this framework to the setting of non-Markovian continuous-time reinforcement learning and provide promising empirical evidence in this direction.