James T. Kwok

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The Hong Kong University of Science and Technology

Professor in the Department of Computer Science and Engineering at Hong Kong University of Science and Technology

Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution

Recently, deep convolutional neural network methods have achieved an exc...
06/13/2021 ∙ by Huapeng Wu, et al. ∙ 0 ∙ share

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Feedback Pyramid Attention Networks for Single Image Super-Resolution

Recently, convolutional neural network (CNN) based image super-resolutio...
06/13/2021 ∙ by Huapeng Wu, et al. ∙ 0 ∙ share

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TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation

In few-shot domain adaptation (FDA), classifiers for the target domain a...
06/11/2021 ∙ by Haoang Chi, et al. ∙ 0 ∙ share

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SparseBERT: Rethinking the Importance Analysis in Self-attention

Transformer-based models are popular for natural language processing (NL...
02/25/2021 ∙ by Han Shi, et al. ∙ 0 ∙ share

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A Survey of Label-noise Representation Learning: Past, Present and Future

Classical machine learning implicitly assumes that labels of the trainin...
11/09/2020 ∙ by Bo Han, et al. ∙ 0 ∙ share

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Efficient Low-Rank Matrix Learning by Factorizable Nonconvex Regularization

Matrix learning is at the core of many machine learning problems. To enc...
08/14/2020 ∙ by Yaqing Wang, et al. ∙ 8 ∙ share

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Effective Decoding in Graph Auto-Encoder using Triadic Closure

The (variational) graph auto-encoder and its variants have been popularl...
11/26/2019 ∙ by Han Shi, et al. ∙ 0 ∙ share

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Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback

Communication overhead is a major bottleneck hampering the scalability o...
05/27/2019 ∙ by Shuai Zheng, et al. ∙ 0 ∙ share

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Blockwise Adaptivity: Faster Training and Better Generalization in Deep Learning

Stochastic methods with coordinate-wise adaptive stepsize (such as RMSpr...
05/23/2019 ∙ by Shuai Zheng, et al. ∙ 0 ∙ share

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General Convolutional Sparse Coding with Unknown Noise

Convolutional sparse coding (CSC) can learn representative shift-invaria...
03/08/2019 ∙ by Yaqing Wang, et al. ∙ 0 ∙ share

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Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data

Variance reduction has been commonly used in stochastic optimization. It...
06/08/2018 ∙ by Shuai Zheng, et al. ∙ 0 ∙ share

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Power Law in Sparsified Deep Neural Networks

The power law has been observed in the degree distributions of many biol...
05/04/2018 ∙ by Lu Hou, et al. ∙ 0 ∙ share

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Online Convolutional Sparse Coding with Sample-Dependent Dictionary

Convolutional sparse coding (CSC) has been popularly used for the learni...
04/27/2018 ∙ by Yaqing Wang, et al. ∙ 0 ∙ share

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Loss-aware Weight Quantization of Deep Networks

The huge size of deep networks hinders their use in small computing devi...
02/23/2018 ∙ by Lu Hou, et al. ∙ 0 ∙ share

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Scalable Robust Matrix Factorization with Nonconvex Loss

Robust matrix factorization (RMF), which uses the ℓ_1-loss, often outper...
10/19/2017 ∙ by Quanming Yao, et al. ∙ 0 ∙ share

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Efficient Robust Matrix Factorization with Nonconvex Loss

Robust matrix factorization (RMF), which uses the ℓ_1-loss, often outper...
10/19/2017 ∙ by Quanming Yao, et al. ∙ 0 ∙ share

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Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers

Low-rank modeling has many important applications in computer vision and...
08/01/2017 ∙ by Quanming Yao, et al. ∙ 0 ∙ share

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Scalable Online Convolutional Sparse Coding

Convolutional sparse coding (CSC) improves sparse coding by learning a s...
06/21/2017 ∙ by Yaqing Wang, et al. ∙ 0 ∙ share

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Multi-Label Learning with Global and Local Label Correlation

It is well-known that exploiting label correlations is important to mult...
04/04/2017 ∙ by Yue Zhu, et al. ∙ 0 ∙ share

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Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion

Matrix and tensor completion aim to recover a low-rank matrix / tensor f...
03/16/2017 ∙ by Quanming Yao, et al. ∙ 0 ∙ share

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Loss-aware Binarization of Deep Networks

Deep neural network models, though very powerful and highly successful, ...
11/05/2016 ∙ by Lu Hou, et al. ∙ 0 ∙ share

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Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity

The use of convex regularizers allows for easy optimization, though they...
06/13/2016 ∙ by Quanming Yao, et al. ∙ 0 ∙ share

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Stochastic Variance-Reduced ADMM

The alternating direction method of multipliers (ADMM) is a powerful opt...
04/24/2016 ∙ by Shuai Zheng, et al. ∙ 0 ∙ share

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Fast Nonsmooth Regularized Risk Minimization with Continuation

In regularized risk minimization, the associated optimization problem be...
02/25/2016 ∙ by Shuai Zheng, et al. ∙ 0 ∙ share

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Fast Low-Rank Matrix Learning with Nonconvex Regularization

Low-rank modeling has a lot of important applications in machine learnin...
12/03/2015 ∙ by Quanming Yao, et al. ∙ 0 ∙ share

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James T. Kwok

Featured Co-authors

Masashi Sugiyama

Jun Zhang

Tie-Yan Liu

Xiaodan Liang

Tongliang Liu

Gang Niu

Feng Liu

Zhi-Hua Zhou

Bo Han

Ivor W. Tsang

Zhenguo Li

Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution

Feedback Pyramid Attention Networks for Single Image Super-Resolution

TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation

SparseBERT: Rethinking the Importance Analysis in Self-attention

A Survey of Label-noise Representation Learning: Past, Present and Future

Efficient Low-Rank Matrix Learning by Factorizable Nonconvex Regularization

Effective Decoding in Graph Auto-Encoder using Triadic Closure

Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback

Blockwise Adaptivity: Faster Training and Better Generalization in Deep Learning

General Convolutional Sparse Coding with Unknown Noise

Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data

Power Law in Sparsified Deep Neural Networks

Online Convolutional Sparse Coding with Sample-Dependent Dictionary

Loss-aware Weight Quantization of Deep Networks

Scalable Robust Matrix Factorization with Nonconvex Loss

Efficient Robust Matrix Factorization with Nonconvex Loss

Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers

Scalable Online Convolutional Sparse Coding

Multi-Label Learning with Global and Local Label Correlation

Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion

Loss-aware Binarization of Deep Networks

Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity

Stochastic Variance-Reduced ADMM

Fast Nonsmooth Regularized Risk Minimization with Continuation

Fast Low-Rank Matrix Learning with Nonconvex Regularization