b'Fanhua Shang'

research

∙ 08/21/2023

Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer

Deep neural networks are vulnerable to adversarial examples crafted by a...

0 Zhijin Ge, et al. ∙

research

∙ 06/08/2023

Boosting Adversarial Transferability by Achieving Flat Local Maxima

Transfer-based attack adopts the adversarial examples generated on the s...

0 Zhijin Ge, et al. ∙

research

∙ 09/25/2022

Exploring Example Influence in Continual Learning

Continual Learning (CL) sequentially learns new tasks like human beings,...

0 Qing Sun, et al. ∙

research

∙ 06/23/2021

Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning

Federated Learning (FL) has become an active and promising distributed m...

0 Hua Huang, et al. ∙

research

∙ 06/22/2021

Learned Interpretable Residual Extragradient ISTA for Sparse Coding

Recently, the study on learned iterative shrinkage thresholding algorith...

0 Lin Kong, et al. ∙

research

∙ 06/18/2021

Quantized Neural Networks via -1, +1 Encoding Decomposition and Acceleration

The training of deep neural networks (DNNs) always requires intensive re...

0 Qigong Sun, et al. ∙

research

∙ 03/22/2021

Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling

Video super-resolution (VSR) aims at restoring a video in low-resolution...

0 Hongying Liu, et al. ∙

research

∙ 03/09/2021

MWQ: Multiscale Wavelet Quantized Neural Networks

Model quantization can reduce the model size and computational latency, ...

0 Qigong Sun, et al. ∙

research

∙ 03/04/2021

Effective and Fast: A Novel Sequential Single Path Search for Mixed-Precision Quantization

Since model quantization helps to reduce the model size and computation ...

0 Qigong Sun, et al. ∙

research

∙ 11/29/2020

Layer Pruning via Fusible Residual Convolutional Block for Deep Neural Networks

In order to deploy deep convolutional neural networks (CNNs) on resource...

0 Pengtao Xu, et al. ∙

research

∙ 10/31/2020

Differentially Private ADMM Algorithms for Machine Learning

In this paper, we study efficient differentially private alternating dir...

18 Tao Xu, et al. ∙

research

∙ 10/21/2020

Boosting Gradient for White-Box Adversarial Attacks

Deep neural networks (DNNs) are playing key roles in various artificial ...

0 Hongying Liu, et al. ∙

research

∙ 08/24/2020

A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution

Spherical videos, also known as 360 (panorama) videos, can be viewed wit...

0 Hongying Liu, et al. ∙

research

∙ 07/25/2020

Video Super Resolution Based on Deep Learning: A comprehensive survey

In recent years, deep learning has made great progress in the fields of ...

0 Hongying Liu, et al. ∙

research

∙ 05/10/2020

A Unified Weight Learning and Low-Rank Regression Model for Robust Face Recognition

Regression-based error modelling has been extensively studied for face r...

0 Miaohua Zhang, et al. ∙

research

∙ 02/27/2020

Deep Residual-Dense Lattice Network for Speech Enhancement

Convolutional neural networks (CNNs) with residual links (ResNets) and c...

0 Mohammad Nikzad, et al. ∙

research

∙ 12/02/2019

Efficient Relaxed Gradient Support Pursuit for Sparsity Constrained Non-convex Optimization

Large-scale non-convex sparsity-constrained problems have recently gaine...

0 Fanhua Shang, et al. ∙

research

∙ 07/21/2019

signADAM: Learning Confidences for Deep Neural Networks

In this paper, we propose a new first-order gradient-based algorithm to ...

0 Dong Wang, et al. ∙

research

∙ 07/17/2019

CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation

This paper proposes a novel cascaded U-Net for brain tumor segmentation....

12 Hongying Liu, et al. ∙

research

∙ 05/31/2019

Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1

The training of deep neural networks (DNNs) requires intensive resources...

0 Qigong Sun, et al. ∙

research

∙ 10/11/2018

Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications

The heavy-tailed distributions of corrupted outliers and singular values...

0 Fanhua Shang, et al. ∙

research

∙ 10/07/2018

ASVRG: Accelerated Proximal SVRG

This paper proposes an accelerated proximal stochastic variance reduced ...

0 Fanhua Shang, et al. ∙

research

∙ 07/26/2018

A Unified Approximation Framework for Deep Neural Networks

Deep neural networks (DNNs) have achieved significant success in a varie...

0 Yuzhe Ma, et al. ∙

research

∙ 07/26/2018

A Unified Approximation Framework for Non-Linear Deep Neural Networks

Deep neural networks (DNNs) have achieved significant success in a varie...

0 Yuzhe Ma, et al. ∙

research

∙ 06/28/2018

A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates

Recent years have witnessed exciting progress in the study of stochastic...

0 Kaiwen Zhou, et al. ∙

research

∙ 02/28/2018

Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization

The Schatten quasi-norm was introduced to bridge the gap between the tra...

0 Fanhua Shang, et al. ∙

research

∙ 02/26/2018

VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning

In this paper, we propose a simple variant of the original SVRG, called ...

0 Fanhua Shang, et al. ∙

research

∙ 02/26/2018

Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization

In this paper, we propose a novel sufficient decrease technique for stoc...

0 Fanhua Shang, et al. ∙

research

∙ 07/11/2017

Accelerated Variance Reduced Stochastic ADMM

Recently, many variance reduced stochastic alternating direction method ...

0 Yuanyuan Liu, et al. ∙

research

∙ 04/17/2017

Larger is Better: The Effect of Learning Rates Enjoyed by Stochastic Optimization with Progressive Variance Reduction

In this paper, we propose a simple variant of the original stochastic va...

0 Fanhua Shang, et al. ∙

research

∙ 03/23/2017

Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning

Recently, research on accelerated stochastic gradient descent methods (e...

0 Fanhua Shang, et al. ∙

research

∙ 03/20/2017

Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient Descent

In this paper, we propose a novel sufficient decrease technique for vari...

0 Fanhua Shang, et al. ∙

research

∙ 06/04/2016

Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization

The Schatten-p quasi-norm (0<p<1) is usually used to replace the standar...

0 Fanhua Shang, et al. ∙

research

∙ 06/02/2016

Unified Scalable Equivalent Formulations for Schatten Quasi-Norms

The Schatten quasi-norm can be used to bridge the gap between the nuclea...

0 Fanhua Shang, et al. ∙

research

∙ 12/26/2015

Regularized Orthogonal Tensor Decompositions for Multi-Relational Learning

Multi-relational learning has received lots of attention from researcher...

0 Fanhua Shang, et al. ∙

research

∙ 09/03/2014

Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations

Recovering low-rank and sparse matrices from incomplete or corrupted obs...

0 Fanhua Shang, et al. ∙

Fanhua Shang

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