bluuebunny/crossref_metadata_2025 huggingface dataset:
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning
Authors: Rong Yi , Lu Xiongbo , Sun Zhaoyang , Chen Yaxiong , Xiong Shengwu
Published: June 2023

Self-supervised learning (SSL) techniques have recently been integrated into the few-shot learning (FSL) framework and have shown promising results in improving the few-shot image classification performance. However, existing SSL approaches used in FSL typically seek the supervision signals from the global embedding of every single image. Therefore, during the episodic training of FSL, these methods cannot capture and fully utilize the local visual information in image samples and the data structure information of the whole episode, which are beneficial to FSL. To this end, we propose to augment the few-shot learning objective with a novel self-supervised Episodic Spatial Pretext Task (ESPT). Specifically, for each few-shot episode, we generate its corresponding transformed episode by applying a random geometric transformation to all the images in it. Based on these, our ESPT objective is defined as maximizing the local spatial relationship consistency between the original episode and the transformed one. With this definition, the ESPT-augmented FSL objective promotes learning more transferable feature representations that capture the local spatial features of different images and their inter-relational structural information in each input episode, thus enabling the model to generalize better to new categories with only a few samples. Extensive experiments indicate that our ESPT method achieves new state-of-the-art performance for few-shot image classification on three mainstay benchmark datasets. The source code will be available at: https://github.com/Whut-YiRong/ESPT.
Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI
Authors: Sia Suzanna , Belyy Anton , Almahairi Amjad , Khabsa Madian , Zettlemoyer Luke , Mathias Lambert
Published: June 2023

Evaluating an explanation's faithfulness is desired for many reasons such as trust, interpretability and diagnosing the sources of model's errors. In this work, which focuses on the NLI task, we introduce the methodology of Faithfulness-through-Counterfactuals, which first generates a counterfactual hypothesis based on the logical predicates expressed in the explanation, and then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic (i.e. if the new formula is \textit{logically satisfiable}). In contrast to existing approaches, this does not require any explanations for training a separate verification model. We first validate the efficacy of automatic counterfactual hypothesis generation, leveraging on the few-shot priming paradigm. Next, we show that our proposed metric distinguishes between human-model agreement and disagreement on new counterfactual input. In addition, we conduct a sensitivity analysis to validate that our metric is sensitive to unfaithful explanations.
Neural Spline Search for Quantile Probabilistic Modeling
Authors: Sun Ruoxi , Li Chun-Liang , Arik Sercan Ö. , Dusenberry Michael W. , Lee Chen-Yu , Pfister Tomas
Published: June 2023

Accurate estimation of output quantiles is crucial in many use cases, where it is desired to model the range of possibility. Modeling target distribution at arbitrary quantile levels and at arbitrary input attribute levels are important to offer a comprehensive picture of the data, and requires the quantile function to be expressive enough. The quantile function describing the target distribution using quantile levels is critical for quantile regression. Although various parametric forms for the distributions (that the quantile function specifies) can be adopted, an everlasting problem is selecting the most appropriate one that can properly approximate the data distributions. In this paper, we propose a non-parametric and data-driven approach,
Neural Spline Search (NSS), to represent the observed data distribution without parametric assumptions. NSS is flexible and expressive for modeling data distributions by transforming the inputs with a series of monotonic spline regressions guided by symbolic operators. We demonstrate that NSS outperforms previous methods on synthetic, real-world regression and time-series forecasting tasks.
DE-net: Dynamic Text-Guided Image Editing Adversarial Networks
Authors: Tao Ming , Bao Bing-Kun , Tang Hao , Wu Fei , Wei Longhui , Tian Qi
Published: June 2023

Text-guided image editing models have shown remarkable results. However, there remain two problems. First, they employ fixed manipulation modules for various editing requirements (e.g., color changing, texture changing, content adding and removing), which results in over-editing or insufficient editing. Second, they do not clearly distinguish between text-required and text-irrelevant parts, which leads to inaccurate editing.
To solve these limitations, we propose:
(i) a Dynamic Editing Block (DEBlock) that composes different editing modules dynamically for various editing requirements.
(ii) a Composition Predictor (Comp-Pred), which predicts the composition weights for DEBlock according to the inference on target texts and source images.
(iii) a Dynamic text-adaptive Convolution Block (DCBlock) that queries source image features to distinguish text-required parts and text-irrelevant parts.
Extensive experiments demonstrate that our DE-Net achieves excellent performance and manipulates source images more correctly and accurately.
Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks
Authors: Tian Bowen , Su Qinliang , Yu Jianxing
Published: June 2023

Generative adversarial networks (GANs) are known for their strong abilities on capturing the underlying distribution of training instances. Since the seminal work of GAN, many variants of GAN have been proposed. However, existing GANs are almost established on the assumption that the training dataset is clean. But in many real-world applications, this may not hold, that is, the training dataset may be contaminated by a proportion of undesired instances. When training on such datasets, existing GANs will learn a mixture distribution of desired and contaminated instances, rather than the desired distribution of desired data only (target distribution). To learn the target distribution from contaminated datasets, two purified generative adversarial networks (PuriGAN) are developed, in which the discriminators are augmented with the capability to distinguish between target and contaminated instances by leveraging an extra dataset solely composed of contamination instances. We prove that under some mild conditions, the proposed PuriGANs are guaranteed to converge to the distribution of desired instances. Experimental results on several datasets demonstrate that the proposed PuriGANs are able to generate much better images from the desired distribution than comparable baselines when trained on contaminated datasets. In addition, we also demonstrate the usefulness of PuriGAN on downstream applications by applying it to the tasks of semi-supervised anomaly detection on contaminated datasets and PU-learning. Experimental results show that PuriGAN is able to deliver the best performance over comparable baselines on both tasks.
Training-Time Attacks against K-nearest Neighbors
Authors: Vartanian Ara , Rosenbaum Will , Alfeld Scott
Published: June 2023

Nearest neighbor-based methods are commonly used for classification tasks and as subroutines of other data-analysis methods.
An attacker with the capability of inserting their own data points into the training set can manipulate the inferred nearest neighbor structure.
We distill this goal to the task of performing a training-set data insertion attack against k-Nearest Neighbor classification (kNN).
We prove that computing an optimal training-time (a.k.a. poisoning) attack against kNN classification is NP-Hard, even when k = 1 and the attacker can insert only a single data point.
We provide an anytime algorithm to perform such an attack, and a greedy algorithm for general k and attacker budget.
We provide theoretical bounds and empirically demonstrate the effectiveness and practicality of our methods on synthetic and real-world datasets.
Empirically, we find that kNN is vulnerable in practice and that dimensionality reduction is an effective defense.
We conclude with a discussion of open problems illuminated by our analysis.
The Implicit Regularization of Momentum Gradient Descent in Overparametrized Models
Authors: Wang Li , Fu Zhiguo , Zhou Yingcong , Yan Zili
Published: June 2023

The study of the implicit regularization induced by gradient-based optimization in deep learning is a long-standing pursuit. In the present paper, we characterize the implicit regularization of momentum gradient descent (MGD) in the continuous-time view, so-called momentum gradient flow (MGF). We show that the components of weight vector are learned for a deep linear neural networks at different evolution rates, and this evolution gap increases with the depth. Firstly, we show that if the depth equals one, the evolution gap between the weight vector components is linear, which is consistent with the performance of ridge. In particular, we establish a tight coupling between MGF and ridge for the least squares regression. In detail, we show that when the regularization parameter of ridge is inversely proportional to the square of the time parameter of MGF, the risk of MGF is no more than 1.54 times that of ridge, and their relative Bayesian risks are almost indistinguishable. Secondly, if the model becomes deeper, i.e. the depth is greater than or equal to 2, the evolution gap becomes more significant, which implies an implicit bias towards sparse solutions. The numerical experiments strongly support our theoretical results.
Hierarchical Contrastive Learning for Temporal Point Processes
Authors: Wang Qingmei , Cheng Minjie , Yuan Shen , Xu Hongteng
Published: June 2023

As an important sequential model, the temporal point process (TPP) plays a central role in real-world sequence modeling and analysis, whose learning is often based on the maximum likelihood estimation (MLE). However, due to imperfect observations, such as incomplete and sparse sequences that are common in practice, the MLE of TPP models often suffers from overfitting and leads to unsatisfactory generalization power. In this work, we develop a novel hierarchical contrastive (HCL) learning method for temporal point processes, which provides a new regularizer of MLE. In principle, our HCL considers the noise contrastive estimation (NCE) problem at the event-level and at the sequence-level jointly. Given a sequence, the event-level NCE maximizes the probability of each observed event given its history while penalizing the conditional probabilities of the unobserved events. At the same time, we generate positive and negative event sequences from the observed sequence and maximize the discrepancy between their likelihoods through the sequence-level NCE. Instead of using time-consuming simulation methods, we generate the positive and negative sequences via a simple but efficient model-guided thinning process. Experimental results show that the MLE method assisted by the HCL regularizer outperforms classic MLE and other contrastive learning methods in learning various TPP models consistently. The code is available at https://github.com/qingmeiwangdaily/HCL_TPP.
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems
Authors: Wu Xidong , Hu Zhengmian , Huang Heng
Published: June 2023

The minimax optimization over Riemannian manifolds (possibly nonconvex constraints) has been actively applied to solve many problems, such as robust dimensionality reduction and deep neural networks with orthogonal weights (Stiefel manifold). Although many optimization algorithms for minimax problems have been developed in the Euclidean setting, it is difficult to convert them into Riemannian cases, and algorithms for nonconvex minimax problems with nonconvex constraints are even rare. On the other hand, to address the big data challenges, decentralized (serverless) training techniques have recently been emerging since they can reduce communications overhead and avoid the bottleneck problem on the server node. Nonetheless, the algorithm for decentralized Riemannian minimax problems has not been studied. In this paper, we study the distributed nonconvex-strongly-concave minimax optimization problem over the Stiefel manifold and propose both deterministic and stochastic minimax methods. The Steifel manifold is a non-convex set. The global function is represented as the finite sum of local functions. For the deterministic setting, we propose DRGDA and prove that our deterministic method achieves a gradient complexity of O( epsilon(-2)) under mild conditions. For the stochastic setting, we propose DRSGDA and prove that our stochastic method achieves a gradient complexity of O( epsilon(-4)). The DRGDA and DRSGDA are the first algorithms for distributed minimax optimization with nonconvex constraints with exact convergence. Extensive experimental results on the Deep Neural Networks (DNNs) training over the Stiefel manifold demonstrate the efficiency of our algorithms.
FedNP: Towards Non-IID Federated Learning via Federated Neural Propagation
Authors: Wu Xueyang , Huang Hengguan , Ding Youlong , Wang Hao , Wang Ye , Xu Qian
Published: June 2023

Traditional federated learning (FL) algorithms, such as FedAvg, fail to handle non-i.i.d data because they learn a global model by simply averaging biased local models that are trained on non-i.i.d local data, therefore failing to model the global data distribution.
In this paper, we present a novel Bayesian FL algorithm that successfully handles such a non-i.i.d FL setting by enhancing the local training task with an auxiliary task that explicitly estimates the global data distribution.
One key challenge in estimating the global data distribution is that the data are partitioned in FL, and therefore the ground-truth global data distribution is inaccessible.
To address this challenge, we propose an expectation-propagation-inspired probabilistic neural network, dubbed federated neural propagation (FedNP), which efficiently estimates the global data distribution given non-i.i.d data partitions. Our algorithm is sampling-free and end-to-end differentiable, can be applied with any conventional FL frameworks and learns richer global data representation.
Experiments on both image classification tasks with synthetic non-i.i.d image data partitions and real-world non-i.i.d speech recognition tasks demonstrate that our framework effectively alleviates the performance deterioration caused by non-i.i.d data.
Zero-Cost Operation Scoring in Differentiable Architecture Search
Authors: Xiang Lichuan , Dudziak Lukasz , Abdelfattah Mohamed S. , Chau Thomas , Lane Nicholas D. , Wen Hongkai
Published: June 2023

We formalize and analyze a fundamental component of dif-
ferentiable neural architecture search (NAS): local “opera-
tion scoring” at each operation choice. We view existing
operation scoring functions as inexact proxies for accuracy,
and we find that they perform poorly when analyzed empir-
ically on NAS benchmarks. From this perspective, we intro-
duce a novel perturbation-based zero-cost operation scor-
ing (Zero-Cost-PT) approach, which utilizes zero-cost prox-
ies that were recently studied in multi-trial NAS but de-
grade significantly on larger search spaces, typical for dif-
ferentiable NAS. We conduct a thorough empirical evalu-
ation on a number of NAS benchmarks and large search
spaces, from NAS-Bench-201, NAS-Bench-1Shot1, NAS-
Bench-Macro, to DARTS-like and MobileNet-like spaces,
showing significant improvements in both search time and
accuracy. On the ImageNet classification task on the DARTS
search space, our approach improved accuracy compared to
the best current training-free methods (TE-NAS) while be-
ing over 10× faster (total searching time 25 minutes on a
single GPU), and observed significantly better transferabil-
ity on architectures searched on the CIFAR-10 dataset with
an accuracy increase of 1.8 pp. Our code is available at:
https://github.com/zerocostptnas/zerocost operation score.
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems
Authors: Xie Jiahao , Zhang Chao , Shen Zebang , Liu Weijie , Qian Hui
Published: June 2023

Minimax problems arise in a wide range of important applications including robust adversarial learning and Generative Adversarial Network (GAN) training. Recently, algorithms for minimax problems in the Federated Learning (FL) paradigm have received considerable interest. Existing federated algorithms for general minimax problems require the full aggregation (i.e., aggregation of local model information from all clients) in each training round. Thus, they are inapplicable to an important setting of FL known as the cross-device setting, which involves numerous unreliable mobile/IoT devices. In this paper, we develop the first practical algorithm named CDMA for general minimax problems in the cross-device FL setting. CDMA is based on a Start-Immediately-With-Enough-Responses mechanism, in which the server first signals a subset of clients to perform local computation and then starts to aggregate the local results reported by clients once it receives responses from enough clients in each round. With this mechanism, CDMA is resilient to the low client availability. In addition, CDMA is incorporated with a lightweight global correction in the local update steps of clients, which mitigates the impact of slow network connections. We establish theoretical guarantees of CDMA under different choices of hyperparameters and conduct experiments on AUC maximization, robust adversarial network training, and GAN training tasks. Theoretical and experimental results demonstrate the efficiency of CDMA.
Towards Optimal Randomized Strategies in Adversarial Example Game
Authors: Xie Jiahao , Zhang Chao , Liu Weijie , Bai Wensong , Qian Hui
Published: June 2023

The vulnerability of deep neural network models to adversarial example attacks is a practical challenge in many artificial intelligence applications. A recent line of work shows that the use of randomization in adversarial training is the key to find optimal strategies against adversarial example attacks. However, in a fully randomized setting where both the defender and the attacker can use randomized strategies, there are no efficient algorithm for finding such an optimal strategy. To fill the gap, we propose the first algorithm of its kind, called FRAT, which models the problem with a new infinite-dimensional continuous-time flow on probability distribution spaces. FRAT maintains a lightweight mixture of models for the defender, with flexibility to efficiently update mixing weights and model parameters at each iteration. Furthermore, FRAT utilizes lightweight sampling subroutines to construct a random strategy for the attacker. We prove that the continuous-time limit of FRAT converges to a mixed Nash equilibria in a zero-sum game formed by a defender and an attacker. Experimental results also demonstrate the efficiency of FRAT on CIFAR-10 and CIFAR-100 datasets.
On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization
Authors: Xin Shiji , Wang Yifei , Su Jingtong , Wang Yisen
Published: June 2023

Despite impressive success in many tasks, deep learning models are shown to rely on spurious features, which will catastrophically fail when generalized to out-of-distribution (OOD) data. Invariant Risk Minimization (IRM) is proposed to alleviate this issue by extracting domain-invariant features for OOD generalization. Nevertheless, recent work shows that IRM is only effective for a certain type of distribution shift (e.g., correlation shift) while it fails for other cases (e.g., diversity shift). Meanwhile, another thread of method, Adversarial Training (AT), has shown better domain transfer performance, suggesting that it has the potential to be an effective candidate for extracting domain-invariant features. This paper investigates this possibility by exploring the similarity between the IRM and AT objectives. Inspired by this connection, we propose Domain-wise Adversarial Training (DAT), an AT-inspired method for alleviating distribution shift by domain-specific perturbations. Extensive experiments show that our proposed DAT can effectively remove domain-varying features and improve OOD generalization under both correlation shift and diversity shift.
Progressive Deep Multi-View Comprehensive Representation Learning
Authors: Xu Cai , Zhao Wei , Zhao Jinglong , Guan Ziyu , Yang Yaming , Chen Long , Song Xiangyu
Published: June 2023

Multi-view Comprehensive Representation Learning (MCRL) aims to synthesize information from multiple views to learn comprehensive representations of data items. Prevalent deep MCRL methods typically concatenate synergistic view-specific representations or average aligned view-specific representations in the fusion stage. However, the performance of synergistic fusion methods inevitably degenerate or even fail when partial views are missing in real-world applications; the aligned based fusion methods usually cannot fully exploit the complementarity of multi-view data. To eliminate all these drawbacks, in this work we present a Progressive Deep Multi-view Fusion (PDMF) method. Considering the multi-view comprehensive representation should contain complete information and the view-specific data contain partial information, we deem that it is unstable to directly learn the mapping from partial information to complete information. Hence, PDMF employs a progressive learning strategy, which contains the pre-training and fine-tuning stages. In the pre-training stage, PDMF decodes the auxiliary comprehensive representation to the view-specific data. It also captures the consistency and complementarity by learning the relations between the dimensions of the auxiliary comprehensive representation and all views. In the fine-tuning stage, PDMF learns the mapping from the original data to the comprehensive representation with the help of the auxiliary comprehensive representation and relations. Experiments conducted on a synthetic toy dataset and 4 real-world datasets show that PDMF outperforms state-of-the-art baseline methods. The code is released at https://github.com/winterant/PDMF.
BridgeTower: Building Bridges between Encoders in Vision-Language Representation Learning
Authors: Xu Xiao , Wu Chenfei , Rosenman Shachar , Lal Vasudev , Che Wanxiang , Duan Nan
Published: June 2023

Vision-Language (VL) models with the Two-Tower architecture have dominated visual-language representation learning in recent years. Current VL models either use lightweight uni-modal encoders and learn to extract, align and fuse both modalities simultaneously in a deep cross-modal encoder, or feed the last-layer uni-modal representations from the deep pre-trained uni-modal encoders into the top cross-modal encoder. Both approaches potentially restrict vision-language representation learning and limit model performance. In this paper, we propose BridgeTower, which introduces multiple bridge layers that build a connection between the top layers of uni-modal encoders and each layer of the cross-modal encoder. This enables effective bottom-up cross-modal alignment and fusion between visual and textual representations of different semantic levels of pre-trained uni-modal encoders in the cross-modal encoder. Pre-trained with only 4M images, BridgeTower achieves state-of-the-art performance on various downstream vision-language tasks. In particular, on the VQAv2 test-std set, BridgeTower achieves an accuracy of 78.73%, outperforming the previous state-of-the-art model METER by 1.09% with the same pre-training data and almost negligible additional parameters and computational costs. Notably, when further scaling the model, BridgeTower achieves an accuracy of 81.15%, surpassing models that are pre-trained on orders-of-magnitude larger datasets. Code and checkpoints are available at https://github.com/microsoft/BridgeTower.
Trusted Fine-Grained Image Classification through Hierarchical Evidence Fusion
Authors: Xu Zhikang , Yue Xiaodong , Lv Ying , Liu Wei , Li Zihao
Published: June 2023

Fine-Grained Image Classification (FGIC) aims to classify images into specific subordinate classes of a superclass. Due to insufficient training data and confusing data samples, FGIC may produce uncertain classification results that are untrusted for data applications. In fact, FGIC can be viewed as a hierarchical classification process and the multilayer information facilitates to reduce uncertainty and improve the reliability of FGIC. In this paper, we adopt the evidence theory to measure uncertainty and confidence in hierarchical classification process and propose a trusted FGIC method through fusing multilayer classification evidence. Comparing with the traditional approaches, the trusted FGIC method not only generates accurate classification results but also reduces the uncertainty of fine-grained classification. Specifically, we construct an evidence extractor at each classification layer to extract multilayer (multi-grained) evidence for image classification. To fuse the extracted multi-grained evidence from coarse to fine, we formulate evidence fusion with the Dirichlet hyper probability distribution and thereby hierarchically decompose the evidence of coarse-grained classes into fine-grained classes to enhance the classification performances. The ablation experiments validate that the hierarchical evidence fusion can improve the precision and also reduce the uncertainty of fine-grained classification. The comparison with state-of-the-art FGIC methods shows that our proposed method achieves competitive performances.
Computably Continuous Reinforcement-Learning Objectives Are PAC-Learnable
Authors: Yang Cambridge , Littman Michael , Carbin Michael
Published: June 2023

In reinforcement learning, the classic objectives of maximizing discounted and finite-horizon cumulative rewards are PAC-learnable: There are algorithms that learn a near-optimal policy with high probability using a finite amount of samples and computation.
In recent years, researchers have introduced objectives and corresponding reinforcement-learning algorithms beyond the classic cumulative rewards, such as objectives specified as linear temporal logic formulas.
However, questions about the PAC-learnability of these new objectives have remained open.

This work demonstrates the PAC-learnability of general reinforcement-learning objectives through sufficient conditions for PAC-learnability in two analysis settings.
In particular, for the analysis that considers only sample complexity, we prove that if an objective given as an oracle is uniformly continuous, then it is PAC-learnable.
Further, for the analysis that considers computational complexity, we prove that if an objective is computable, then it is PAC-learnable.
In other words, if a procedure computes successive approximations of the objective's value, then the objective is PAC-learnable.

We give three applications of our condition on objectives from the literature with previously unknown PAC-learnability and prove that these objectives are PAC-learnable.
Overall, our result helps verify existing objectives' PAC-learnability.
Also, as some studied objectives that are not uniformly continuous have been shown to be not PAC-learnable, our results could guide the design of new PAC-learnable objectives.
CEM: Constrained Entropy Maximization for Task-Agnostic Safe Exploration
Authors: Yang Qisong , Spaan Matthijs T.J.
Published: June 2023

In the absence of assigned tasks, a learning agent typically seeks to explore its environment efficiently. However, the pursuit of exploration will bring more safety risks.
An under-explored aspect of reinforcement learning is how to achieve safe efficient exploration when the task is unknown.
In this paper, we propose a practical Constrained Entropy Maximization (CEM) algorithm to solve task-agnostic safe exploration problems, which naturally require a finite horizon and undiscounted constraints on safety costs.
The CEM algorithm aims to learn a policy that maximizes state entropy under the premise of safety.
To avoid approximating the state density in complex domains, CEM leverages a k-nearest neighbor entropy estimator to evaluate the efficiency of exploration.
In terms of safety, CEM minimizes the safety costs, and adaptively trades off safety and exploration based on the current constraint satisfaction. The empirical analysis shows that CEM enables the acquisition of a safe exploration policy in complex environments, resulting in improved performance in both safety and sample efficiency for target tasks.
T-distributed Spherical Feature Representation for Imbalanced Classification
Authors: Yang Xiaoyu , Chen Yufei , Yue Xiaodong , Xu Shaoxun , Ma Chao
Published: June 2023

Real-world classification tasks often show an extremely imbalanced problem. The extreme imbalance will cause a strong bias that the decision boundary of the classifier is completely dominated by the categories with abundant samples, which are also called the head categories. Current methods have alleviated the imbalanced impact from mainly three aspects: class re-balance, decoupling and domain adaptation. However, the existing criterion with the winner-take-all strategy still leads to the crowding problem in the eigenspace. The head categories with many samples can extract features more accurately, but occupy most of the eigenspace. The tail categories sharing the rest of the narrow eigenspace are too crowded together to accurately extract features. Above these issues, we propose a novel T-distributed spherical metric for equalized eigenspace in the imbalanced classification, which has the following innovations: 1) We design the T-distributed spherical metric, which has the characteristics of high kurtosis. Instead of the winner-take-all strategy, the T-distributed spherical metric produces a high logit only when the extracted feature is close enough to the category center, without a strong bias against other categories. 2) The T-distributed spherical metric is integrated into the classifier, which is able to equalize the eigenspace for alleviating the crowding issue in the imbalanced problem. The equalized eigenspace by the T-distributed spherical classifier is capable of improving the accuracy of the tail categories while maintaining the accuracy of the head, which significantly promotes the intraclass compactness and interclass separability of features. Extensive experiments on large-scale imbalanced datasets verify our method, which shows superior results in the long-tailed CIFAR-100/-10 with the imbalanced ratio IR = 100/50. Our method also achieves excellent results on the large-scale ImageNet-LT dataset and the iNaturalist dataset with various backbones. In addition, we provide a case study of the real clinical classification of pancreatic tumor subtypes with 6 categories. Among them, the largest number of PDAC accounts for 315 cases, and the least CP has only 8 cases. After 4-fold cross-validation, we achieved a top-1 accuracy of 69.04%.
Cluster-Guided Contrastive Graph Clustering Network
Authors: Yang Xihong , Liu Yue , Zhou Sihang , Wang Siwei , Tu Wenxuan , Zheng Qun , Liu Xinwang , Fang Liming , Zhu En
Published: June 2023

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms. The code of CCGC is available at https://github.com/xihongyang1999/CCGC on Github.
Lifelong Variational Autoencoder via Online Adversarial Expansion Strategy
Authors: Ye Fei , Bors Adrian G.
Published: June 2023

The Variational Autoencoder (VAE) suffers from a significant loss of information when trained on a non-stationary data distribution. This loss in VAE models, called catastrophic forgetting, has not been studied theoretically before. We analyse the forgetting behaviour of a VAE in continual generative modelling by developing a new lower bound on the data likelihood, which interprets the forgetting process as an increase in the probability distance between the generator's distribution and the evolved data distribution. The proposed bound shows that a VAE-based dynamic expansion model can achieve better performance if its capacity increases appropriately considering the shift in the data distribution. Based on this analysis, we propose a novel expansion criterion that aims to preserve the information diversity among the VAE components, while ensuring that it acquires more knowledge with fewer parameters. Specifically, we implement this expansion criterion from the perspective of a multi-player game and propose the Online Adversarial Expansion Strategy (OAES), which considers all previously learned components as well as the currently updated component as multiple players in a game, while an adversary model evaluates their performance. The proposed OAES can dynamically estimate the discrepancy between each player and the adversary without accessing task information. This leads to the gradual addition of new components while ensuring the knowledge diversity among all of them. We show theoretically and empirically that the proposed extension strategy can enable a VAE model to achieve the best performance given an appropriate model size.
Random Walk Conformer: Learning Graph Representation from Long and Short Range
Authors: Yeh Pei-Kai , Chen Hsi-Wen , Chen Ming-Syan
Published: June 2023

While graph neural networks (GNNs) have achieved notable success in various graph mining tasks, conventional GNNs only model the pairwise correlation in 1-hop neighbors without considering the long-term relations and the high-order patterns, thus limiting their performances. Recently, several works have addressed these issues by exploring the motif, i.e., frequent subgraphs. However, these methods usually require an unacceptable computational time to enumerate all possible combinations of motifs. In this paper, we introduce a new GNN framework, namely Random Walk Conformer (RWC), to exploit global correlations and local patterns based on the random walk, which is a promising method to discover the graph structure. Besides, we propose random walk encoding to help RWC capture topological information, which is proven more expressive than conventional spatial encoding. Extensive experiment results manifest that RWC achieves state-of-the-art performance on graph classification and regression tasks. The source code of RWC is available at https://github.com/b05901024/RandomWalkConformer.
Policy-Based Primal-Dual Methods for Convex Constrained Markov Decision Processes
Authors: Ying Donghao , Guo Mengzi Amy , Ding Yuhao , Lavaei Javad , Shen Zuo-Jun
Published: June 2023

We study convex Constrained Markov Decision Processes (CMDPs) in which the objective is concave and the constraints are convex in the state-action occupancy measure. We propose a policy-based primal-dual algorithm that updates the primal variable via policy gradient ascent and updates the dual variable via projected sub-gradient descent. Despite the loss of additivity structure and the nonconvex nature, we establish the global convergence of the proposed algorithm by leveraging a hidden convexity in the problem, and prove the O(T^-1/3) convergence rate in terms of both optimality gap and constraint violation. When the objective is strongly concave in the occupancy measure, we prove an improved convergence rate of O(T^-1/2). By introducing a pessimistic term to the constraint, we further show that a zero constraint violation can be achieved while preserving the same convergence rate for the optimality gap. This work is the first one in the literature that establishes non-asymptotic convergence guarantees for policy-based primal-dual methods for solving infinite-horizon discounted convex CMDPs.
Stable Learning via Sparse Variable Independence
Authors: Yu Han , Cui Peng , He Yue , Shen Zheyan , Lin Yong , Xu Renzhe , Zhang Xingxuan
Published: June 2023

The problem of covariate-shift generalization has attracted intensive research attention. Previous stable learning algorithms employ sample reweighting schemes to decorrelate the covariates when there is no explicit domain information about training data. However, with finite samples, it is difficult to achieve the desirable weights that ensure perfect independence to get rid of the unstable variables. Besides, decorrelating within stable variables may bring about high variance of learned models because of the over-reduced effective sample size. A tremendous sample size is required for these algorithms to work. In this paper, with theoretical justification, we propose SVI (Sparse Variable Independence) for the covariate-shift generalization problem. We introduce sparsity constraint to compensate for the imperfectness of sample reweighting under the finite-sample setting in previous methods. Furthermore, we organically combine independence-based sample reweighting and sparsity-based variable selection in an iterative way to avoid decorrelating within stable variables, increasing the effective sample size to alleviate variance inflation. Experiments on both synthetic and real-world datasets demonstrate the improvement of covariate-shift generalization performance brought by SVI.
CEMA – Cost-Efficient Machine-Assisted Document Annotations
Authors: Yuan Guowen , Kao Ben , Wu Tien-Hsuan
Published: June 2023

We study the problem of semantically annotating textual documents that are complex in the sense that the documents are long, feature rich, and domain specific. Due to their complexity, such annotation tasks require trained human workers, which are very expensive in both time and money. We propose CEMA, a method for deploying machine learning to assist humans in complex document annotation. CEMA estimates the human cost of annotating each document and selects the set of documents to be annotated that strike the best balance between model accuracy and human cost. We conduct experiments on complex annotation tasks in which we compare CEMA against other document selection and annotation strategies. Our results show that CEMA is the most cost-efficient solution for those tasks.
Quantum Multi-Agent Meta Reinforcement Learning
Authors: Yun Won Joon , Park Jihong , Kim Joongheon
Published: June 2023

Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of practical quantum computing. Motivated by this, in this article we re-design multi-agent reinforcement learning (MARL) based on the unique characteristics of quantum neural networks (QNNs) having two separate dimensions of trainable parameters: angle parameters affecting the output qubit states, and pole parameters associated with the output measurement basis. Exploiting this dyadic trainability as meta-learning capability, we propose quantum meta MARL (QM2ARL) that first applies angle training for meta-QNN learning, followed by pole training for few-shot or local-QNN training. To avoid overfitting, we develop an angle-to-pole regularization technique injecting noise into the pole domain during angle training. Furthermore, by exploiting the pole as the memory address of each trained QNN, we introduce the concept of pole memory allowing one to save and load trained QNNs using only two-parameter pole values. We theoretically prove the convergence of angle training under the angle-to-pole regularization, and by simulation corroborate the effectiveness of QM2ARL in achieving high reward and fast convergence, as well as of the pole memory in fast adaptation to a time-varying environment.
Imbalanced Label Distribution Learning
Authors: Zhao Xingyu , An Yuexuan , Xu Ning , Wang Jing , Geng Xin
Published: June 2023

Label distribution covers a certain number of labels, representing the degree to which each label describes an instance. The learning process on the instances labeled by label distributions is called Label Distribution Learning (LDL). Although LDL has been applied successfully to many practical applications, one problem with existing LDL methods is that they are limited to data with balanced label information. However, annotation information in real-world data often exhibits imbalanced distributions, which significantly degrades the performance of existing methods. In this paper, we investigate the Imbalanced Label Distribution Learning (ILDL) problem. To handle this challenging problem, we delve into the characteristics of ILDL and empirically find that the representation distribution shift is the underlying reason for the performance degradation of existing methods. Inspired by this finding, we present a novel method named Representation Distribution Alignment (RDA). RDA aligns the distributions of feature representations and label representations to alleviate the impact of the distribution gap between the training set and the test set caused by the imbalance issue. Extensive experiments verify the superior performance of RDA. Our work fills the gap in benchmarks and techniques for practical ILDL problems.
Gradient-Adaptive Pareto Optimization for Constrained Reinforcement Learning
Authors: Zhou Zixian , Huang Mengda , Pan Feiyang , He Jia , Ao Xiang , Tu Dandan , He Qing
Published: June 2023

Constrained Reinforcement Learning (CRL) burgeons broad interest in recent years, which pursues maximizing long-term returns while constraining costs. Although CRL can be cast as a multi-objective optimization problem, it is still facing the key challenge that gradient-based Pareto optimization methods tend to stick to known Pareto-optimal solutions even when they yield poor returns (e.g., the safest self-driving car that never moves) or violate the constraints (e.g., the record-breaking racer that crashes the car). In this paper, we propose Gradient-adaptive Constrained Policy Optimization (GCPO for short), a novel Pareto optimization method for CRL with two adaptive gradient recalibration techniques. First, to find Pareto-optimal solutions with balanced performance over all targets, we propose gradient rebalancing which forces the agent to improve more on under-optimized objectives at every policy iteration. Second, to guarantee that the cost constraints are satisfied, we propose gradient perturbation that can temporarily sacrifice the returns for costs. Experiments on the SafetyGym benchmarks show that our method consistently outperforms previous CRL methods in reward while satisfying the constraints.
Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA
Authors: Zhu Yongxin , Liu Zhen , Liang Yukang , Li Xin , Liu Hao , Bao Changcun , Xu Linli
Published: June 2023

In this paper, we propose a novel multi-modal framework for Scene Text Visual Question Answering (STVQA), which requires models to read scene text in images for question answering. Apart from text or visual objects, which could exist independently, scene text naturally links text and visual modalities together by conveying linguistic semantics while being a visual object in an image simultaneously. Different to conventional STVQA models which take the linguistic semantics and visual semantics in scene text as two separate features, in this paper, we propose a paradigm of "Locate Then Generate" (LTG), which explicitly unifies this two semantics with the spatial bounding box as a bridge connecting them. Specifically, at first, LTG locates the region in an image that may contain the answer words with an answer location module (ALM) consisting of a region proposal network and a language refinement network, both of which can transform to each other with one-to-one mapping via the scene text bounding box. Next, given the answer words selected by ALM, LTG generates a readable answer sequence with an answer generation module (AGM) based on a pre-trained language model. As a benefit of the explicit alignment of the visual and linguistic semantics, even without any scene text based pre-training tasks, LTG can boost the absolute accuracy by +6.06% and +6.92% on the TextVQA dataset and the ST-VQA dataset respectively, compared with a non-pre-training baseline. We further demonstrate that LTG effectively unifies visual and text modalities through the spatial bounding box connection, which is underappreciated in previous methods.
Factors Influencing high adolescent pregnancy rate in Riobamba, Ecuador
Authors: Haus Kaleigh M , House Hans R , Martínez Pablo , Schuette Allison , Kneese Garrett S.
Published: June 2023

The country of Ecuador has the highest rate of adolescent pregnancy in Latin America and the Caribbean. Risk factors associated with this pregnancy rate need to be explored to effectively implement educational youth programs.1 Early sexual initiation, poor reproductive health knowledge, and disruption of family structure are hypothesized to be major risk factors of adolescent (<20 years) motherhood (AMH) in Riobamba, Ecuador.2 We conducted a case-control study with case being women <20 years of age during first pregnancy and control being women ≥20 years of age during first pregnancy. Controls were not matched. The study took place in June/July of 2021 in five public governmental primary health clinics within Riobamba, Ecuador with the primary goal of determining risk factors associated with AMH. Age responses were used to designate group status. Risk factors were analyzed using Welch’s two-sample t-tests and Fisher’s Exact tests. 198 participants were analyzed: 99 who had AMH and 99 who had non-AMH. Adolescent mothers were significantly younger during first sexual encounter than adult mothers. Adolescent mothers were also more likely to lack reproductive health knowledge and have a disruption in family structure. While our study was able to broadly explore the risk factors for AMH, the underlying causes of these risk factors, such as societal pressures, need to be examined further. Early education about sexual health and use of contraception should be an initial goal of Riobamba’s programs to reduce AMH.