GenAI4ELab/papercli-papers huggingface dataset:
Learning to Boost Resilience of Complex Networks via Neural Edge Rewiring
Authors: Shanchao Yang; MA KAILI; Tianshu Yu; Baoxiang Wang; Hongyuan Zha
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The resilience of complex networks, a critical structural characteristic in network science, measures the network's ability to withstand noise corruption and structural changes. Improving resilience typically resorts to minimal modifications of the network structure via degree-preserving edge rewiring-based methods. Despite their effectiveness, existing methods are learning-free, sharing the limitation of transduction: a learned edge rewiring strategy from one graph cannot be generalized to another. Such a limitation cannot be trivially addressed by existing graph neural networks (GNNs)-based approaches since there is no rich initial node features for GNNs to learn meaningful representations. However, neural edge rewiring relies on GNNs for obtaining meaningful representations from pure graph topologies to select edges. We found existing GNNs degenerate remarkably with only pure topologies on the resilience task, leading to the undesired infinite action backtracking. In this work, inspired by persistent homology, we specifically design a variant of GNN called FireGNN for learning inductive edge rewiring strategies. Based on meaningful representations from FireGNN, we develop the first end-to-end inductive method, ResiNet, to discover $\textbf{resi}$lient $\textbf{net}$work topologies while balancing network utility. ResiNet reformulates network resilience optimization as a Markov decision process equipped with edge rewiring action space and learns to select correct edges successively.  Extensive experiments demonstrate that ResiNet achieves a near-optimal resilience gain on various graphs while balancing the utility and outperforms existing approaches by a large margin.
$\mathrm{SE}(3)$-Equivariant Attention Networks for Shape Reconstruction in Function Space
Authors: Evangelos Chatzipantazis; Stefanos Pertigkiozoglou; Edgar Dobriban; Kostas Daniilidis
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We propose a method for 3D shape reconstruction from unoriented point clouds. Our method consists of a novel SE(3)-equivariant coordinate-based network (TF-ONet), that parametrizes the occupancy field of the shape and respects the inherent symmetries of the problem. In contrast to previous shape reconstruction methods that align the input to a regular grid, we operate directly on the irregular point cloud. Our architecture leverages equivariant attention layers that operate on local tokens. This mechanism enables local shape modelling, a crucial property for scalability to large scenes. Given an unoriented, sparse, noisy point cloud as input, we produce equivariant features for each point. These serve as keys and values for the subsequent equivariant cross-attention blocks that parametrize the occupancy field. By querying an arbitrary point in space, we predict its occupancy score. We show that our method outperforms previous SO(3)-equivariant methods, as well as non-equivariant methods trained on SO(3)-augmented datasets. More importantly, local modelling together with SE(3)-equivariance create an ideal setting for SE(3) scene reconstruction. We show that by training only on single, aligned objects and without any pre-segmentation, we can reconstruct novel scenes containing arbitrarily many objects in random poses without any performance loss.
Efficient Discrete Multi Marginal Optimal Transport Regularization
Authors: Ronak Mehta; Jeffery Kline; Vishnu Suresh Lokhande; Glenn Fung; Vikas Singh
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Optimal transport has emerged as a powerful tool for a variety of problems in machine learning, and it is frequently used to enforce distributional constraints. In this context, existing methods often use either a Wasserstein metric, or else they apply concurrent barycenter approaches when more than two distributions are considered. In this paper, we  leverage multi-marginal optimal transport (MMOT), where we take advantage of a procedure that computes a generalized earth mover's distance as a sub-routine. We show that not only is our algorithm computationally more efficient compared to other barycentric-based distance methods, but it has the additional advantage that gradients used for backpropagation can be efficiently computed during the forward pass computation itself, which leads to substantially faster model training. We provide technical details about this new regularization term and its properties, and we present experimental demonstrations of faster runtimes when compared to standard Wasserstein-style methods. Finally, on a range of experiments designed to assess effectiveness at enforcing fairness, we demonstrate our method compares well with alternatives.
Offline Communication Learning with Multi-source Datasets
Authors: Yihuan Mao; Rui Hu; Lulu Zheng; Jianhao Wang; Chongjie Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Scalability and partial observability are two major challenges faced by multi-agent reinforcement learning. Recently researchers propose offline MARL algorithms to improve scalability by reducing online exploration cost, while the problem of partial observability is often ignored in the offline MARL setting. Communication is a promising approach to alleviate the miscoordination caused by partially observability, thus in this paper we focus on offline communication learning where agents learn from an fixed dataset. We find out that learning communications in an end-to-end manner from a given offline dateset without communication information is intractable, since the correct communication protocol space is too sparse compared with the exponentially growing joint state-action space when the number of agents increases. Besides, unlike offline policy learning which can be guided by reward signals, offline communication learning is struggling since communication messages implicitly impact the reward. Moreover, in real-world applications, offline MARL datasets are often collected from multi-source, leaving offline MARL communication learning more challenging. Therefore, we present a new benchmark which contains a diverse set of challenging offline MARL communication tasks with single/multi-source datasets, and propose a novel Multi-Head structure for Communication Imitation learning (MHCI) algorithm that automatically adapts to the distribution of the dataset. Empirical result shows the effectiveness of our method on various tasks of the new offline communication learning benchmark.
Chopping Formers is what you need in Vision
Authors: Francesca Babiloni; Thomas Tanay; Matteo Maggioni; Jiankang Deng; Ales Leonardis; Stefanos Zafeiriou
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: This work presents a new dynamic and fully-connected layer (DFC) that generalizes existing layers and is free from hard inductive biases. Then, it describes how to factorize the DFC weights efficiently.
Using the Einstein convention as framework, we define the DFC as a fully connected layer with the weight tensor created as a function of the input. DFC is the non-linear extension of the most general case of linear layer for neural network, and therefore all major neural network layers, from convolution to self-attention, are particular cases of DFCs. A stack of DFCs interleaved by non-linearities defines a new super-class of neural networks: \emph{Formers}.
DFC has four major characteristics: it is Dynamic and Spatially Adaptive, it has a Global Receptive Field, and it mixes all the available channels' information. 
In their complete form, DFCs are powerful layers free from hard inductive biases, but their use is limited in practice by their prohibitive computational cost. To overcome this limitation and deploy DFC in real computer-vision applications, we propose to use the CP decomposition, showing that it is possible to factorize the DFC layer into smaller, manageable blocks without losing any representational power. Finally, we propose ChoP'D Former, an architecture making use of a new decomposition of the DFC layer into five sequential operations, each incorporating one characteristic of the original DFC tensor. Chop'D Former leverages dynamic gating and integral image, achieves global spatial reasoning with constant time complexity, and has a receptive field that can adapt depending on the task. Extensive experiments demonstrate that our ChoP'D Former is competitive with state-of-the-art results on three well-known computer vision benchmarks, namely Large-Scale Classification, Object Detection, and Instance Segmentation, suppressing the need for expensive architecture search and hyperparameter optimization.
Adaptive Update Direction Rectification for Unsupervised Continual Learning
Authors: Yizhao Gao; Nanyi Fei; Zhiwu Lu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recent works on continual learning have shown that unsupervised continual learning (UCL) methods rival or even beat supervised continual learning methods. However, most UCL methods typically adopt fixed learning strategies with pre-defined objectives and ignore the influence of the constant shift of data distributions on the newer training process. This non-adaptive paradigm tends to achieve sub-optimal performance, since the optimal update direction (to ensure the trade-off between old and new tasks) keeps changing during training over sequential tasks. In this work, we thus propose a novel UCL framework termed AUDR to adaptively rectify the update direction by a policy network (i.e., the Actor) at each training step based on the reward predicted by a value network (i.e., the Critic). Concretely, different from existing Actor-Critic based reinforcement learning works, there are three vital designs that make our AUDR applicable to the UCL setting: (1) A reward function to measure the score/value of the currently selected action, which provides the ground-truth reward to guide the Critic's predictions; (2) An action space for the Actor to select actions (i.e., update directions) according to the reward predicted by the Critic; (3) A multinomial sampling strategy with a lower-bound on the sampling probability of each action, which is designed to improve the variance of the Actor's selected actions for more diversified exploration. Extensive experiments show that our AUDR achieves state-of-the-art results under both the in-dataset and cross-dataset UCL settings. Importantly, our AUDR also shows superior performance when combined with other UCL methods, which suggests that our AUDR is highly extensible and versatile.
Multi-scale Attention for Diabetic Retinopathy Detection in Retinal Fundus Images
Authors: Temitope Ibrahim Amosa; Patrick Sebastian; Lila Iznita Izhar; Fatimat Adeola Adekola; Mardiyyah Adeola Salahudeen
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The diagnosis and/or grading of diabetic retinopathy (DR) in the retina fundus has traditionally been done by physicians using manual procedures. However, there has been a significant demand for automated eye diagnostic and grading systems due to the constant rise in the number of persons with diabetes over the past few decades. An excellent diagnostic and predictive value for treatment planning exists with automatic DR grading based on retinal fundus pictures. With the majority of the current automated DR grading systems, it is exceedingly challenging to capture significant features because of the minor changes between severity levels. This paper presents a deep learning-based method for automatically assessing diabetic retinopathy in retina fundus pictures. This paper presents a deep learning-based method for automatically assessing diabetic retinopathy in retina fundus pictures. In order to increase the discriminative ability of the retrieved features, we implement a multi-scale attention mechanism within a deep convolutional neural network architecture in this research. Additionally, we provide a brand-new loss function termed modified grading loss that enhances the training convergence of the suggested strategy by taking into account the distance between various grades of distinct DR categories. The suggested technique is trained, validated, and tested using a dataset about diabetic retinopathy that is openly available. The experimental findings are presented to illustrate how well the suggested strategy competes.
A second order regression model shows edge of stability behavior
Authors: Atish Agarwala; Jeffrey Pennington; Fabian Pedregosa
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recent studies of learning algorithms have shown that there is a regime with an initial increase in the largest eigenvalue of the loss Hessian (progressive sharpening), followed by a stabilization of the eigenvalue near the maximum value which allows convergence (edge of stability). We consider a class of predictive models that are quadratic in the parameters, which we call second-order regression models. This is in contrast with the neural tangent kernel regime, where the predictive function is linear in the parameters. For quadratic objectives in two dimensions, we prove that this second order regression model exhibits both progressive sharpening and edge of stability behavior. We then show that in higher dimensions, the model shows this behavior generically without the structure of a neural network, due to a non-linearity induced in the learning dynamics. Finally, we show that edge of stability behavior in neural networks is correlated with the behavior in quadratic regression models.
Federated Nearest Neighbor Machine Translation
Authors: Yichao Du; Zhirui Zhang; Bingzhe Wu; Lemao Liu; Tong Xu; Enhong Chen
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention. Training neural machine translation (NMT) models with traditional FL algorithm (e.g., FedAvg) typically relies on multi-round model-based interactions. However, it is impractical and inefficient for machine translation tasks due to the vast communication overheads and heavy synchronization. In this paper, we propose a novel federated nearest neighbor (FedNN) machine translation framework that, instead of multi-round model-based interactions, leverages one-round memorization-based interaction to share knowledge across different clients to build low-overhead privacy-preserving systems. The whole approach equips the public NMT model trained on large-scale accessible data with a $k$-nearest-neighbor ($k$NN) classifier and integrates the external datastore constructed by private text data in all clients to form the final FL model.  A two-phase datastore encryption strategy is introduced to achieve privacy-preserving during this process.  Extensive experiments show that FedNN significantly reduces computational and communication costs compared with FedAvg, while maintaining promising performance in different FL settings.
Causal Confusion and Reward Misidentification in Preference-Based Reward Learning
Authors: Jeremy Tien; Jerry Zhi-Yang He; Zackory Erickson; Anca Dragan; Daniel S. Brown
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Learning policies via preference-based reward learning is an increasingly popular method for customizing agent behavior, but has been shown anecdotally to be prone to spurious correlations and reward hacking behaviors. While much prior work focuses on causal confusion in reinforcement learning and behavioral cloning, we focus on a systematic study of causal confusion and reward misidentification when learning from preferences. In particular, we perform a series of sensitivity and ablation analyses on several benchmark domains where rewards learned from preferences achieve minimal test error but fail to generalize to out-of-distribution states---resulting in poor policy performance when optimized. We find that the presence of non-causal distractor features, noise in the stated preferences, and partial state observability can all exacerbate reward misidentification. We also identify a set of methods with which to interpret misidentified learned rewards. In general, we observe that optimizing misidentified rewards drives the policy off the reward's training distribution, resulting in high predicted (learned) rewards but low true rewards. These findings illuminate the susceptibility of preference learning to reward misidentification and causal confusion---failure to consider even one of many factors can result in unexpected, undesirable behavior.
MaskFusion: Feature Augmentation for Click-Through Rate Prediction via Input-adaptive Mask Fusion
Authors: Chao Liao; Jianchao Tan; Jiyuan Jia; Yi Guo; Chengru Song
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Click-through rate (CTR) prediction plays important role in the advertisement, recommendation, and retrieval applications. Given the feature set, how to fully utilize the information from the feature set is an active topic in deep CTR model designs. There are several existing deep CTR works focusing on feature interactions, feature attentions, and so on. They attempt to capture high-order feature interactions to enhance the generalization ability of deep CTR models. However, these works either suffer from poor high-order feature interaction modeling using DNN or ignore the balance between generalization and memorization during the recommendation. To mitigate these problems, we propose an adaptive feature fusion framework called MaskFusion, to additionally capture the explicit interactions between the input feature and the existing deep part structure of deep CTR models dynamically, besides the common feature interactions proposed in existing works.  MaskFusion is an instance-aware feature augmentation method, which makes deep CTR models more personalized by assigning each feature with an instance-adaptive mask and fusing each feature with each hidden state vector in the deep part structure. MaskFusion can also be integrated into any existing deep CTR models flexibly. MaskFusion achieves state-of-the-art (SOTA) performance on all seven benchmarks deep CTR models with three public datasets.
A New Paradigm for Federated Structure Non-IID Subgraph Learning
Authors: Xunkai Li; Wentao Zhang; Rong-Hua Li; Yulin Zhao; Yinlin Zhu; Guoren Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Federated graph learning (FGL), a distributed training framework for graph neural networks (GNNs) has attracted much attention for breaking the centralized machine learning assumptions. Despite its effectiveness, the differences in data collection perspectives and quality lead to the challenges of heterogeneity, especially the domain-specific graph is partitioned into subgraphs in different institutions. However, existing FGL methods implement graph data augmentation or personalization with community split which follows the cluster homogeneity assumptions. Hence we investigate the above issues and suggest that subgraph heterogeneity is essentially the structure variations. From the observations on FGL, we first define the structure non-independent identical distribution (Non-IID) problem, which presents covariant shift challenges among client-wise subgraphs. Meanwhile, we propose a new paradigm for general federated data settings called Adaptive Federated Graph Learning (AdaFGL). The motivation behind it is to implement adaptive propagation mechanisms based on federated global knowledge and non-params label propagation. We conduct extensive experiments with community split and structure Non-IID settings, our approach achieves state-of-the-art performance on five benchmark datasets.
VectorMapNet: End-to-end Vectorized HD Map Learning
Authors: Yicheng Liu; Tianyuan Yuan; Yue Wang; Yilun Wang; Hang Zhao
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Autonomous driving systems require a good understanding of surrounding environments, including moving obstacles and static High-Definition (HD) semantic map elements. Existing methods approach the semantic map problem by offline manual annotation, which suffers from serious scalability issues.  Recent learning-based methods produce dense rasterized segmentation predictions to construct maps. However, these predictions do not include instance information of individual map elements and require heuristic post-processing to obtain vectorized maps. To tackle these challenges, we introduce an end-to-end vectorized HD map learning pipeline, termed VectorMapNet. VectorMapNet takes onboard sensor observations and predicts a sparse set of polylines in the bird's-eye view. This pipeline can explicitly model the spatial relation between map elements and generate vectorized maps that are friendly to downstream autonomous driving tasks. Extensive experiments show that VectorMapNet achieve strong map learning performance on both nuScenes and Argoverse2 dataset, surpassing previous state-of-the-art methods by 14.2 mAP and 14.6mAP. Qualitatively, we also show that VectorMapNet is capable of generating comprehensive maps and capturing more fine-grained details of road geometry. 
To the best of our knowledge, VectorMapNet is the first work designed towards end-to-end vectorized map learning from onboard sensors.
$\sigma$Reparam: Stable Transformer Training with Spectral Reparametrization
Authors: Shuangfei Zhai; Tatiana Likhomanenko; Etai Littwin; Jason Ramapuram; Dan Busbridge; Yizhe Zhang; Jiatao Gu; Joshua M. Susskind
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Training stability is of great importance to Transformers. In this work, we investigate the training dynamics of Transformers by examining the evolution of the attention layers. In particular, we track the "attention entropy" for each attention head during the course of training, which is a proxy of the attention's sharpness. We observe a common, non monotonic evolution of attention entropy across different settings: the attention entropy first quickly decreases in the initial phase of training, followed by quickly increasing, and finally entering a long stable phase. While the exact shape can be affected by hyperparameters such as warmup, initialization, learning rate etc., we found that there is a close correlation between the minima of attention entropy and the model's training stability. To this end, we propose a simple and efficient solution dubbed $\sigma$Reparam, where we reparametrize all linear layers with Spectral Normalization and an additional learned scalar. We provide a lower bound on the attention entropy as a function of the spectral norms of the query and key projections, which suggests that small attention entropy can be obtained with large spectral norms. $\sigma$Reparam decouples the growth rate of a weight matrix's spectral norm from its dimensionality, which we verify empirically. We conduct experiments with $\sigma$Reparam on image classification, image self supervised learning, automatic speech recognition and language modeling tasks. We show that $\sigma$Reparam provides great stability and robustness with respect to the choice of hyperparameters.