GenAI4ELab/papercli-papers huggingface dataset:
Learning to Communicate using Contrastive Learning 
Authors: Yat Long Lo; Biswa Sengupta; Jakob Nicolaus Foerster; Michael Noukhovitch
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Communication is a powerful tool for coordination in multi-agent RL.  Inducing an effective, common language has been a difficult challenge, particularly in the decentralized setting. In this work, we introduce an alternative perspective where communicative messages sent between agents are considered as different incomplete views of the environment state. Based on this perspective, we propose to learn to communicate using contrastive learning by maximizing the mutual information between messages of a given trajectory. In communication-essential environments, our method outperforms previous work in both performance and learning speed. Using qualitative metrics and representation probing, we show that our method induces more symmetric communication and captures task-relevant information from the environment. Finally, we demonstrate promising results on zero-shot communication, a first for MARL. Overall, we show the power of contrastive learning, and self-supervised learning in general, as a method for learning to communicate.
Posterior Sampling Model-based Policy Optimization under Approximate Inference
Authors: Chaoqi Wang; Yuxin Chen; Kevin Patrick Murphy
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Model-based reinforcement learning algorithms (MBRL) hold tremendous promise for improving the sample efficiency in online RL. However, many existing popular MBRL algorithms cannot deal with exploration and exploitation properly. Posterior sampling reinforcement learning (PSRL) serves as a promising approach for automatically trading off the exploration and exploitation, but the theoretical guarantees only hold under exact inference. In this paper, we show that adopting the same methodology as in exact PSRL can be fairly suboptimal under approximate inference. Motivated by the analysis, we propose an improved factorization for the posterior distribution of polices by removing the conditional independence between the policy and data given the model. By adopting such a posterior factorization, we further propose a general algorithmic framework for PSRL under approximate inference and a practical instantiation of it. Empirically, our algorithm can surpass the baseline methods by a significant margin on both dense rewards and sparse rewards tasks from DM control suite, OpenAI Gym and Metaworld benchmarks.
Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient Representations
Authors: Ziyu Jiang; Yinpeng Chen; Mengchen Liu; Dongdong Chen; Xiyang Dai; Lu Yuan; Zicheng Liu; Zhangyang Wang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Recently, both Contrastive Learning (CL) and Mask Image Modeling (MIM) demonstrate that self-supervision is powerful to learn good representations. However, naively combining them is far from success. In this paper, we start by making the empirical observation that a naive joint optimization of CL and MIM losses leads to conflicting gradient directions - more severe as the layers go deeper. This motivates us to shift the paradigm from combining loss at the end, to choosing the proper learning method per network layer. Inspired by experimental observations, we find that MIM and CL are suitable to lower and higher layers, respectively. We hence propose to combine them in a surprisingly simple, ``sequential cascade'' fashion: early layers are first trained under one MIM loss, on top of which latter layers continue to be trained under another CL loss. The proposed Layer Grafted Pre-training learns good visual representations that demonstrate superior label efficiency in downstream applications, in particular yielding strong few-shot performance besides linear evaluation. For instance, on ImageNet-1k, Layer Grafted Pre-training yields 65.5% Top-1 accuracy in terms of 1% few-shot learning with ViT-B/16, which improves MIM and CL baselines by 14.4% and 2.1% with no bells and whistles. The code is available at https://github.com/VITA-Group/layerGraftedPretraining_ICLR23.git.
Holistically Explainable Vision Transformers
Authors: Moritz Böhle; Mario Fritz; Bernt Schiele
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Transformers increasingly dominate the machine learning landscape across many tasks and domains, which increases the importance for understanding their outputs. While their attention modules provide partial insight into their inner workings, the attention scores have been shown to be insufficient for explaining the models as a whole. To address this, we propose B-cos transformers, which inherently provide holistic explanations for their decisions. Specifically, we formulate each model component—such as the multi-layer perceptrons, attention layers, and the  tokenisation module—to be dynamic linear, which allows us to faithfully summarise the entire transformer via a single linear transform. We apply our proposed design to Vision Transformers (ViTs) and show that the resulting models, dubbed Bcos-ViTs, are highly interpretable and perform competitively to baseline ViTs on ImageNet. Code will be available at: github.com/anonymous/authors.
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
Authors: Haixu Wu; Tengge Hu; Yong Liu; Hang Zhou; Jianmin Wang; Mingsheng Long
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Time series analysis is of immense importance in extensive applications, such as weather forecasting, anomaly detection, and action recognition. This paper focuses on temporal variation modeling, which is the common key problem of extensive analysis tasks. Previous methods attempt to accomplish this directly from the 1D time series, which is extremely challenging due to the intricate temporal patterns. Based on the observation of multi-periodicity in time series, we ravel out the complex temporal variations into the multiple intraperiod- and interperiod-variations. To tackle the limitations of 1D time series in representation capability, we extend the analysis of temporal variations into the 2D space by transforming the 1D time series into a set of 2D tensors based on multiple periods. This transformation can embed the intraperiod- and interperiod-variations into the columns and rows of the 2D tensors respectively, making the 2D-variations to be easily modeled by 2D kernels. Technically, we propose the TimesNet with TimesBlock as a task-general backbone for time series analysis. TimesBlock can discover the multi-periodicity adaptively and extract the complex temporal variations from transformed 2D tensors by a parameter-efficient inception block. Our proposed TimesNet achieves consistent state-of-the-art in five mainstream time series analysis tasks, including short- and long-term forecasting, imputation, classification, and anomaly detection. Code is available at this repository: https://github.com/thuml/TimesNet.
Grounding Graph Network Simulators using Physical Sensor Observations
Authors: Jonas Linkerhägner; Niklas Freymuth; Paul Maria Scheikl; Franziska Mathis-Ullrich; Gerhard Neumann
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Physical simulations that accurately model reality are crucial for many engineering disciplines such as mechanical engineering and robotic motion planning. In recent years, learned Graph Network Simulators produced accurate mesh-based simulations while requiring only a fraction of the computational cost of traditional simulators. Yet, the resulting predictors are confined to learning from data generated by existing mesh-based simulators and thus cannot include real world sensory information such as point cloud data. As these predictors have to simulate complex physical systems from only an initial state, they exhibit a high error accumulation for long-term predictions. In this work, we integrate sensory information to ground Graph Network Simulators on real world observations. In particular, we predict the mesh state of deformable objects by utilizing point cloud data. The resulting model allows for accurate predictions over longer time horizons, even under uncertainties in the simulation, such as unknown material properties. Since point clouds are usually not available for every time step, especially in online settings, we employ an imputation-based model. The model can make use of such additional information only when provided, and resorts to a standard Graph Network Simulator, otherwise. We experimentally validate our approach on a suite of prediction tasks for mesh-based interactions between soft and rigid bodies. Our method results in utilization of additional point cloud information to accurately predict stable simulations where existing Graph Network Simulators fail.
Task-Agnostic Online Meta-Learning in Non-stationary Environments
Authors: Daouda Sow; Sen Lin; Yingbin Liang; Junshan Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Online meta-learning has recently emerged as a marriage between batch meta-learning and online learning, for achieving the capability of quick adaptation on new tasks in a lifelong manner. However, most existing approaches focus on the restrictive setting where the distribution of the online tasks remains fixed with known task boundaries. In this work we relax these assumptions and propose a novel algorithm for task-agnostic online meta-learning in non-stationary environments. More specifically, we first propose two simple but effective detection mechanisms of task switches and distribution shift based on empirical observations, which serve as a key building block for more elegant online model updates in our algorithm: the task switch detection mechanism allows reusing of the best model available for the current task at hand, and the distribution shift detection mechanism differentiates the meta model update so as to preserve the knowledge for in-distribution tasks and quickly learn the new knowledge for out-of-distribution tasks.
Motivated by the recent advance in online learning, our online meta model updates are based only on the current data, which eliminates the need of storing previous data as required in most existing methods. This crucial choice is also well supported by our theoretical analysis of dynamic regret in online meta-learning, where a sublinear regret can be achieved by updating the meta model at each round using the current data only. Empirical studies on three different benchmarks clearly demonstrate the significant advantage of our algorithm over related baseline approaches.
Comparative Analysis between Vision Transformers and CNNs from the view of Neuroscience
Authors: Xiuyuan Hu; Hao Zhang; Yang Zhao
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Neuroscience has provide many inspirations for the development of artificial intelligence, especially for neural networks for computer vision tasks. Recent research on animals' visual systems builds the connection between neural sparsity and animals' levels of evolution, based on which comparisons between two most influential vision architecture, Transformer and CNN, are carried out. In particular, the sparsity of attentions in Transformers is comprehensively studied, and previous knowledge on sparsity of neurons in CNNs is reviewed. In addition, a novel metric for neural sparsity is defined and ablation experiments are launched on various types of Transformer and CNN models. Finally, we draw the conclusion that more layers in models will result in higher sparsity, however, too many heads in Transformers may cause reduction of sparsity, which attributes to the significant overlap among effects of attention units.
Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs
Authors: Yuan Cheng; Ruiquan Huang; Yingbin Liang; Jing Yang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In reward-free reinforcement learning (RL), an agent explores the environment first without any reward information, in order to achieve certain learning goals afterwards for any given reward. In this paper we focus on reward-free RL under low-rank MDP models, in which both the representation and linear weight vectors are unknown. Although various algorithms have been proposed for reward-free low-rank MDPs, the corresponding sample complexity is still far from being satisfactory. In this work, we first provide the first known sample complexity lower bound that holds for any algorithm under low-rank MDPs. This lower bound implies it is strictly harder to find a near-optimal policy under low-rank MDPs than under linear MDPs. We then propose a novel model-based algorithm, coined RAFFLE, and show it can both find an $\epsilon$-optimal policy and achieve an $\epsilon$-accurate system identification via reward-free exploration, with a sample complexity significantly improving the previous results. Such a sample complexity matches our lower bound in the dependence on $\epsilon$, as well as on $K$ {in the large $d$ regime}, where $d$ and $K$ respectively denote the representation dimension and action space cardinality. Finally, we provide a planning algorithm (without further interaction with true environment) for RAFFLE to learn a near-accurate representation, which is the first known representation learning guarantee under the same setting.
Partial Label Unsupervised Domain Adaptation with Class-Prototype Alignment
Authors: Yan Yan; Yuhong Guo
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Partial label learning (PLL) tackles the problem where each instance is associated with a set of candidate labels, only one of which is the ground-truth label. Most existing PLL approaches assume that both the training and test sets share an identical data distribution. However, this assumption does not hold in many real-world scenarios where the training and test data come from different distributions. In this paper, we formalize this learning scenario as a new problem called partial label unsupervised domain adaptation (PLUDA). To address this challenging PLUDA problem, we propose a novel Prototype Alignment based PLUDA method named PAPLUDA, which dynamically refines the pseudo-labels of instances from both the source and target domains by consulting the outputs of a teacher-student model in a moving-average manner, and bridges the cross-domain discrepancy through inter-domain class-prototype alignment. In addition, a teacher-student model based contrastive regularization is deployed to enhance prediction stability and hence improve the class-prototypes in both domains for PLUDA. Comprehensive experimental results demonstrate that PAPLUDA achieves state-of-the-art performance on the widely used benchmark datasets.
Efficient Edge Inference by Selective Query
Authors: Anil Kag; Igor Fedorov; Aditya Gangrade; Paul Whatmough; Venkatesh Saligrama
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Edge devices provide inference on predictive tasks to many end-users. However, deploying deep neural networks that achieve state-of-the-art accuracy on these devices is infeasible due to edge resource constraints. Nevertheless, cloud-only processing, the de-facto standard, is also problematic, since uploading large amounts of data imposes severe communication bottlenecks. We propose a novel end-to-end hybrid learning framework that allows the edge to selectively query only those hard examples that the cloud can classify correctly. Our framework optimizes over neural architectures and trains edge predictors and routing models so that the overall accuracy remains high while minimizing the overall latency. Training a hybrid learner is difficult since we lack annotations of hard edge-examples. We introduce a novel proxy supervision in this context and show that our method adapts seamlessly and near optimally across different latency regimes. On the ImageNet dataset, our proposed method deployed on a micro-controller unit exhibits $25\%$ reduction in latency compared to cloud-only processing while suffering no excess loss.
A Hierarchical Hyper-rectangle Mass Model for Fine-grained Entity Typing
Authors: SHING HU; Chunyu Li; Bibo Hao
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Fine-grained entity typing is the task of detecting types of entities inside a given language text. Entity typing models typically transform entities into vectors in high-dimensional space, hyperbolic space, or add additional context information. However, such spaces or feature transformations are not compatible with modeling types' inter-dependencies and diverse scenarios. We study the ability of the hierarchical hyper-rectangle mass model(hRMM), which represents mentions and types into hyper-rectangle mass(hRM) and thus captures the relationships of ontology into a geometric mass view. Natural language contexts are fed into the encoder and then projected to hyper-rectangle mass embedding(hRME). We find that hRM perfectly depicts features of mentions and types. With further research in hypervolume indicators and adaptive thresholds, performance achieves additional improvement. Experiments show that our approach achieves better performance on several entity typing benchmarks and attains state-of-the-art results on two benchmark datasets.
Dealing with missing data using attention and latent space regularization
Authors: Jahan Che Penny-Dimri; Christoph Bergmeir; Julian Smith
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Most practical data science problems encounter missing data. A wide variety of solutions exist, each with strengths and weaknesses that depend upon the missingness-generating process. Here we develop a theoretical framework for training and inference using only observed variables enabling modeling of incomplete datasets without imputation. Using an information and measure-theoretic argument we construct models with latent space representations that regularize against the potential bias introduced by missing data. The theoretical properties of this approach are demonstrated empirically using a synthetic dataset. The performance of this approach is tested on 11 benchmarking datasets with missingness and 18 datasets corrupted across three missingness patterns with comparison against a state-of-the-art model and industry-standard imputation. We show that our proposed method outperforms common imputation methods and the current state-of-the-art with statistical significance.
The Right Losses for the Right Gains: Improving the Semantic Consistency of Deep Text-to-Image Generation with Distribution-Sensitive Losses
Authors: Mahmoud Ahmed; Omer Moussa; Ismail Shaheen; Mohamed O Abdelfattah; Amr Adel Abdalla; Marwan Eid; Hesham Mohamed Eraqi; Mohamed N. Moustafa
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: One of the major challenges in training deep neural networks for text-to-image generation is the significant linguistic discrepancy between ground-truth captions of each image in most popular datasets. The large difference in the choice of words in such captions results in synthesizing images that are semantically dissimilar to each other and to their ground-truth counterparts. Moreover, existing models either fail to generate the fine-grained details of the image or require a huge number of parameters that renders them inefficient for text-to-image synthesis. To fill this gap in the literature, we propose using the contrastive learning approach with a novel combination of two loss functions:  fake-to-fake loss to increase the semantic consistency between generated images of the same caption, and fake-to-real loss to reduce the gap between the distributions of real images and fake ones. We test this approach on two baseline models: SSAGAN and AttnGAN (with style blocks to enhance the fine-grained details of the images.) Results show that our approach improves the qualitative results on AttnGAN with style blocks on the CUB dataset. Additionally, on the challenging COCO dataset, our approach achieves competitive results against the state-of-the-art Lafite model, outperforms the FID scores of SSAGAN and DALL-E models by 44% and 66.83% respectively, yet with only around 1% of the model size and training data of the huge DALL-E model.
Visual Classification via Description from Large Language Models
Authors: Sachit Menon; Carl Vondrick
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: Vision-language models such as CLIP have shown promising performance on a variety of recognition tasks using the standard zero-shot classification procedure -- computing similarity between the query image and the embedded words for each category. By only using the category name, they neglect to make use of the rich context of additional information that language affords. The procedure gives no intermediate understanding of why a category is chosen, and furthermore provides no mechanism for adjusting the criteria used towards this decision. We present an alternative framework for classification with VLMs, which we call classification by description. We ask VLMs to check for descriptive features rather than broad categories: to find a tiger, look for its stripes; its claws; and more. By basing decisions on these descriptors, we can provide additional cues that encourage using the features we want to be used. In the process, we can get a clear idea of what the model ``thinks" it is seeing to make its decision; it gains some level of inherent explainability. We query large language models (e.g., GPT-3) for these descriptors to obtain them in a scalable way. Extensive experiments show our framework has numerous advantages past interpretability. We show improvements in accuracy on ImageNet across distribution shifts; demonstrate the ability to adapt VLMs to recognize concepts unseen during training; and illustrate how descriptors can be edited to effectively mitigate bias compared to the baseline.
Learning Representations for Reinforcement Learning with Hierarchical Forward Models
Authors: Trevor McInroe; Lukas Schäfer; Stefano V Albrecht
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Learning control from pixels is difficult for reinforcement learning (RL) agents because representation learning and policy learning are intertwined. Previous approaches remedy this issue with auxiliary representation learning tasks, but they either do not consider the temporal aspect of the problem or only consider single-step transitions, which may miss relevant information if important environmental changes take many steps to manifest. We propose Hierarchical $k$-Step Latent (HKSL), an auxiliary task that learns representations via a hierarchy of forward models that operate at varying magnitudes of step skipping while also learning to communicate between levels in the hierarchy. We evaluate HKSL in a suite of 30 robotic control tasks with and without distractors and a task of our creation. We find that HKSL either converges to higher episodic returns or optimal performance more quickly than several current baselines. Furthermore, we find that HKSL's representations capture task-relevant details accurately across timescales (even in the presence of distractors) and that communication channels between hierarchy levels organize information based on both sides of the communication process, both of which improve sample efficiency.