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08/21/2023
Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer
Deep neural networks are vulnerable to adversarial examples crafted by a...
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06/08/2023
Boosting Adversarial Transferability by Achieving Flat Local Maxima
Transfer-based attack adopts the adversarial examples generated on the s...
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09/25/2022
Exploring Example Influence in Continual Learning
Continual Learning (CL) sequentially learns new tasks like human beings,...
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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...
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06/22/2021
Learned Interpretable Residual Extragradient ISTA for Sparse Coding
Recently, the study on learned iterative shrinkage thresholding algorith...
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06/18/2021
Quantized Neural Networks via -1, +1 Encoding Decomposition and Acceleration
The training of deep neural networks (DNNs) always requires intensive re...
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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...
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03/09/2021
MWQ: Multiscale Wavelet Quantized Neural Networks
Model quantization can reduce the model size and computational latency, ...
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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 ...
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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...
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10/31/2020
Differentially Private ADMM Algorithms for Machine Learning
In this paper, we study efficient differentially private alternating dir...
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10/21/2020
Boosting Gradient for White-Box Adversarial Attacks
Deep neural networks (DNNs) are playing key roles in various artificial ...
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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...
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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 ...
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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...
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02/27/2020
Deep Residual-Dense Lattice Network for Speech Enhancement
Convolutional neural networks (CNNs) with residual links (ResNets) and c...
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12/02/2019
Efficient Relaxed Gradient Support Pursuit for Sparsity Constrained Non-convex Optimization
Large-scale non-convex sparsity-constrained problems have recently gaine...
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07/21/2019
signADAM: Learning Confidences for Deep Neural Networks
In this paper, we propose a new first-order gradient-based algorithm to ...
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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....
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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...
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10/11/2018
Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications
The heavy-tailed distributions of corrupted outliers and singular values...
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10/07/2018
ASVRG: Accelerated Proximal SVRG
This paper proposes an accelerated proximal stochastic variance reduced ...
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07/26/2018
A Unified Approximation Framework for Deep Neural Networks
Deep neural networks (DNNs) have achieved significant success in a varie...
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07/26/2018
A Unified Approximation Framework for Non-Linear Deep Neural Networks
Deep neural networks (DNNs) have achieved significant success in a varie...
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06/28/2018
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
Recent years have witnessed exciting progress in the study of stochastic...
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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...
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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 ...
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02/26/2018
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization
In this paper, we propose a novel sufficient decrease technique for stoc...
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07/11/2017
Accelerated Variance Reduced Stochastic ADMM
Recently, many variance reduced stochastic alternating direction method ...
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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...
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03/23/2017
Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning
Recently, research on accelerated stochastic gradient descent methods (e...
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03/20/2017
Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient Descent
In this paper, we propose a novel sufficient decrease technique for vari...
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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...
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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...
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12/26/2015
Regularized Orthogonal Tensor Decompositions for Multi-Relational Learning
Multi-relational learning has received lots of attention from researcher...
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09/03/2014
