903 citations · 2.5k across the 78 of their papers we have counts for
43 papers · 1 filter
Generative Oversampling for Imbalanced Data via Majority-Guided VAE
Qingzhong Ai, Pengyun Wang, Lirong He +3
Learning with imbalanced data is a challenging problem in deep learning. Over-sampling is a widely used technique to re-balance the sampling distribution of training data. However,…
MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing
Zhe Li, Zhongwen Rao, Lujia Pan +1
Multivariate time series forecasting has been widely used in various practical scenarios. Recently, Transformer-based models have shown significant potential in forecasting tasks d…
Tensor Networks Meet Neural Networks: A Survey and Future Perspectives
Maolin Wang, Yu Pan, Zenglin Xu +4
Tensor networks (TNs) and neural networks (NNs) are two fundamental data modeling approaches. TNs were introduced to solve the curse of dimensionality in large-scale tensors by con…
Ti-MAE: Self-Supervised Masked Time Series Autoencoders
Zhe Li, Zhongwen Rao, Lujia Pan +2
Multivariate Time Series forecasting has been an increasingly popular topic in various applications and scenarios. Recently, contrastive learning and Transformer-based models have…
Contrastive Multi-view Hyperbolic Hierarchical Clustering
Fangfei Lin, Bing Bai, Kun Bai +3
Hierarchical clustering recursively partitions data at an increasingly finer granularity. In real-world applications, multi-view data have become increasingly important. This raise…
A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks
Yu Pan, Zeyong Su, Ao Liu +3
Tensorial Convolutional Neural Networks (TCNNs) have attracted much research attention for their power in reducing model parameters or enhancing the generalization ability. However…
Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training
Shenglai Zeng, Zonghang Li, Hongfang Yu +4
Federated learning (FL) is a rapidly growing privacy-preserving collaborative machine learning paradigm. In practical FL applications, local data from each data silo reflect local…
Label-Aware Distribution Calibration for Long-tailed Classification
Chaozheng Wang, Shuzheng Gao, Cuiyun Gao +4
Real-world data usually present long-tailed distributions. Training on imbalanced data tends to render neural networks perform well on head classes while much worse on tail classes…
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs
Langzhang Liang, Cuiyun Gao, Shiyi Chen +5
Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, l…
FedLab: A Flexible Federated Learning Framework
Dun Zeng, Siqi Liang, Xiangjing Hu +2
Federated learning (FL) is a machine learning field in which researchers try to facilitate model learning process among multiparty without violating privacy protection regulations.…
Stein Variational Gradient Descent with Multiple Kernel
Qingzhong Ai, Shiyu Liu, Lirong He +1
Stein variational gradient descent (SVGD) and its variants have shown promising successes in approximate inference for complex distributions. In practice, we notice that the kernel…
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE
Qingzhong Ai, Lirong He, Shiyu Liu +1
Recent studies show that advanced priors play a major role in deep generative models. Exemplar VAE, as a variant of VAE with an exemplar-based prior, has achieved impressive result…
Discrete Auto-regressive Variational Attention Models for Text Modeling
Xianghong Fang, Haoli Bai, Jian Li +3
Variational autoencoders (VAEs) have been widely applied for text modeling. In practice, however, they are troubled by two challenges: information underrepresentation and posterior…
TedNet: A Pytorch Toolkit for Tensor Decomposition Networks
Yu Pan, Maolin Wang, Zenglin Xu
Tensor Decomposition Networks (TDNs) prevail for their inherent compact architectures. To give more researchers a flexible way to exploit TDNs, we present a Pytorch toolkit named T…
Partial Differential Equations is All You Need for Generating Neural Architectures -- A Theory for Physical Artificial Intelligence Systems
Ping Guo, Kaizhu Huang, Zenglin Xu
In this work, we generalize the reaction-diffusion equation in statistical physics, Schrödinger equation in quantum mechanics, Helmholtz equation in paraxial optics into the neural…
A Survey on Deep Semi-supervised Learning
Xiangli Yang, Zixing Song, Irwin King +1
Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in…
Graph-based Semi-supervised Learning: A Comprehensive Review
Zixing Song, Xiangli Yang, Zenglin Xu +1
Semi-supervised learning (SSL) has tremendous value in practice due to its ability to utilize both labeled data and unlabelled data. An important class of SSL methods is to natural…
Block-term Tensor Neural Networks
Jinmian Ye, Guangxi Li, Di Chen +3
Deep neural networks (DNNs) have achieved outstanding performance in a wide range of applications, e.g., image classification, natural language processing, etc. Despite the good pe…
Structured Graph Learning for Clustering and Semi-supervised Classification
Zhao Kang, Chong Peng, Qiang Cheng +4
Graphs have become increasingly popular in modeling structures and interactions in a wide variety of problems during the last decade. Graph-based clustering and semi-supervised cla…
Deep Embedded Multi-view Clustering with Collaborative Training
Jie Xu, Yazhou Ren, Guofeng Li +3
Multi-view clustering has attracted increasing attentions recently by utilizing information from multiple views. However, existing multi-view clustering methods are either with hig…
Relation-Guided Representation Learning
Zhao Kang, Xiao Lu, Jian Liang +2
Deep auto-encoders (DAEs) have achieved great success in learning data representations via the powerful representability of neural networks. But most DAEs only focus on the most do…
Discrete Variational Attention Models for Language Generation
Xianghong Fang, Haoli Bai, Zenglin Xu +2
Variational autoencoders have been widely applied for natural language generation, however, there are two long-standing problems: information under-representation and posterior col…
Multi-view Subspace Clustering via Partition Fusion
Juncheng Lv, Zhao Kang, Boyu Wang +2
Multi-view clustering is an important approach to analyze multi-view data in an unsupervised way. Among various methods, the multi-view subspace clustering approach has gained incr…
Structure Learning with Similarity Preserving
Zhao Kang, Xiao Lu, Yiwei Lu +2
Leveraging on the underlying low-dimensional structure of data, low-rank and sparse modeling approaches have achieved great success in a wide range of applications. However, in man…