b'Xiaolin Huang'

research

∙ 08/30/2023

Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs

Multitask learning (MTL) leverages task-relatedness to enhance performan...

0 Jiani Liu, et al. ∙

research

∙ 03/26/2023

Multi-Frame Self-Supervised Depth Estimation with Multi-Scale Feature Fusion in Dynamic Scenes

Multi-frame methods improve monocular depth estimation over single-frame...

0 Jiquan Zhong, et al. ∙

research

∙ 02/28/2023

PCR-CG: Point Cloud Registration via Deep Color and Geometry

In this paper, we introduce PCR-CG: a novel 3D point cloud registration ...

0 Yu Zhang, et al. ∙

research

∙ 02/23/2023

Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective

Although fast adversarial training provides an efficient approach for bu...

1 Zhengbao He, et al. ∙

research

∙ 11/29/2022

A Decentralized Framework for Kernel PCA with Projection Consensus Constraints

This paper studies kernel PCA in a decentralized setting, where data are...

0 Fan He, et al. ∙

research

∙ 11/27/2022

Dynamics of Fecal Coliform Bacteria along Canada's Coast

The vast coastline provides Canada with a flourishing seafood industry i...

0 Shuai You, et al. ∙

research

∙ 11/22/2022

Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors

As data become increasingly vital for deep learning, a company would be ...

0 Sizhe Chen, et al. ∙

research

∙ 11/21/2022

Efficient Generalization Improvement Guided by Random Weight Perturbation

To fully uncover the great potential of deep neural networks (DNNs), var...

0 Tao Li, et al. ∙

research

∙ 09/27/2022

FG-UAP: Feature-Gathering Universal Adversarial Perturbation

Deep Neural Networks (DNNs) are susceptible to elaborately designed pert...

0 Zhixing Ye, et al. ∙

research

∙ 09/18/2022

Random Fourier Features for Asymmetric Kernels

The random Fourier features (RFFs) method is a powerful and popular tech...

0 Mingzhen He, et al. ∙

research

∙ 08/12/2022

Unifying Gradients to Improve Real-world Robustness for Deep Networks

The wide application of deep neural networks (DNNs) demands an increasin...

0 Yingwen Wu, et al. ∙

research

∙ 07/04/2022

WPPG Net: A Non-contact Video Based Heart Rate Extraction Network Framework with Compatible Training Capability

Our facial skin presents subtle color change known as remote Photoplethy...

0 Weiyu Sun, et al. ∙

research

∙ 06/18/2022

Piecewise Linear Neural Networks and Deep Learning

As a powerful modelling method, PieceWise Linear Neural Networks (PWLNNs...

0 Qinghua Tao, et al. ∙

research

∙ 05/26/2022

Trainable Weight Averaging for Fast Convergence and Better Generalization

Stochastic gradient descent (SGD) and its variants are commonly consider...

7 Tao Li, et al. ∙

research

∙ 05/24/2022

One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks

Unlearnable examples (ULEs) aim to protect data from unauthorized usage ...

0 Shutong Wu, et al. ∙

research

∙ 05/24/2022

Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks

The score-based query attacks (SQAs) pose practical threats to deep neur...

0 Sizhe Chen, et al. ∙

research

∙ 02/03/2022

Learning with Asymmetric Kernels: Least Squares and Feature Interpretation

Asymmetric kernels naturally exist in real life, e.g., for conditional p...

0 Mingzhen He, et al. ∙

research

∙ 11/24/2021

Subspace Adversarial Training

Single-step adversarial training (AT) has received wide attention as it ...

0 Tao Li, et al. ∙

research

∙ 09/30/2021

Semi-tensor Product-based TensorDecomposition for Neural Network Compression

The existing tensor networks adopt conventional matrix product for conne...

0 Hengling Zhao, et al. ∙

research

∙ 05/31/2021

Adaptive Feature Alignment for Adversarial Training

Recent studies reveal that Convolutional Neural Networks (CNNs) are typi...

6 PetsTime, et al. ∙

research

∙ 05/31/2021

Dominant Patterns: Critical Features Hidden in Deep Neural Networks

In this paper, we find the existence of critical features hidden in Deep...

0 Zhixing Ye, et al. ∙

research

∙ 05/31/2021

QueryNet: An Efficient Attack Framework with Surrogates Carrying Multiple Identities

Deep Neural Networks (DNNs) are acknowledged as vulnerable to adversaria...

0 Sizhe Chen, et al. ∙

research

∙ 05/02/2021

Residual Enhanced Multi-Hypergraph Neural Network

Hypergraphs are a generalized data structure of graphs to model higher-o...

2 Jing Huang, et al. ∙

research

∙ 04/13/2021

Towards Unbiased Random Features with Lower Variance For Stationary Indefinite Kernels

Random Fourier Features (RFF) demonstrate wellappreciated performance in...

4 Qin Luo, et al. ∙

research

∙ 03/22/2021

Weighted Neural Tangent Kernel: A Generalized and Improved Network-Induced Kernel

The Neural Tangent Kernel (NTK) has recently attracted intense study, as...

0 Lei Tan, et al. ∙

research

∙ 03/20/2021

Train Deep Neural Networks in 40-D Subspaces

Although there are massive parameters in deep neural networks, the train...

4 Tao Li, et al. ∙

research

∙ 02/20/2021

Going Far Boosts Attack Transferability, but Do Not Do It

Deep Neural Networks (DNNs) could be easily fooled by Adversarial Exampl...

0 Sizhe Chen, et al. ∙

research

∙ 12/10/2020

Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT

Training convolutional neural networks (CNNs) for segmentation of pulmon...

3 Yulei Qin, et al. ∙

research

∙ 11/03/2020

Towards a Unified Quadrature Framework for Large-Scale Kernel Machines

In this paper, we develop a quadrature framework for large-scale kernel ...

0 Fanghui Liu, et al. ∙

research

∙ 10/23/2020

Learn Robust Features via Orthogonal Multi-Path

It is now widely known that by adversarial attacks, clean images with in...

4 Kun Fang, et al. ∙

research

∙ 10/22/2020

One-shot Distributed Algorithm for Generalized Eigenvalue Problem

Nowadays, more and more datasets are stored in a distributed way for the...

4 Kexin Lv, et al. ∙

research

∙ 09/10/2020

End-to-end Kernel Learning via Generative Random Fourier Features

Random Fourier features enable researchers to build feature map to learn...

0 Kun Fang, et al. ∙

research

∙ 08/16/2020

Attack on Multi-Node Attention for Object Detection

This paper focuses on high-transferable adversarial attacks on detection...

74 Sizhe Chen, et al. ∙

research

∙ 06/01/2020

Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces

In this paper, we study the asymptotical properties of least squares reg...

0 Fanghui Liu, et al. ∙

research

∙ 05/30/2020

Generalizing Random Fourier Features via Generalized Measures

We generalize random Fourier features, that usually require kernel funct...

0 Fanghui Liu, et al. ∙

research

∙ 05/06/2020

A Communication-Efficient Distributed Algorithm for Kernel Principal Component Analysis

Principal Component Analysis (PCA) is a fundamental technology in machin...

5 Fan He, et al. ∙

research

∙ 04/23/2020

Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond

Random features is one of the most sought-after research topics in stati...

0 Fanghui Liu, et al. ∙

research

∙ 04/23/2020

Sparse Generalized Canonical Correlation Analysis: Distributed Alternating Iteration based Approach

Sparse canonical correlation analysis (CCA) is a useful statistical tool...

6 Jia Cai, et al. ∙

research

∙ 03/19/2020

Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention

With the popularity of stereo cameras in computer assisted surgery techn...

3 Tianyi Zhang, et al. ∙

research

∙ 03/17/2020

Real-time Image Smoothing via Iterative Least Squares

Edge-preserving image smoothing is a fundamental procedure for many comp...

0 Wei Liu, et al. ∙

research

∙ 03/04/2020

Double Backpropagation for Training Autoencoders against Adversarial Attack

Deep learning, as widely known, is vulnerable to adversarial samples. Th...

0 Chengjin Sun, et al. ∙

research

∙ 03/04/2020

Type I Attack for Generative Models

Generative models are popular tools with a wide range of applications. N...

0 Chengjin Sun, et al. ∙

research

∙ 01/21/2020

Generate High-Resolution Adversarial Samples by Identifying Effective Features

As the prevalence of deep learning in computer vision, adversarial sampl...

19 Sizhe Chen, et al. ∙

research

∙ 01/16/2020

Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet

Adversarial attacks on deep neural networks (DNNs) have been found for s...

2 Sizhe Chen, et al. ∙

research

∙ 12/29/2019

Mixed-Precision Quantized Neural Network with Progressively Decreasing Bitwidth For Image Classification and Object Detection

Efficient model inference is an important and practical issue in the dep...

10 Tianshu Chu, et al. ∙

research

∙ 12/16/2019

DAmageNet: A Universal Adversarial Dataset

It is now well known that deep neural networks (DNNs) are vulnerable to ...

0 Sizhe Chen, et al. ∙

research

∙ 11/20/2019

Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

In this paper, we propose a fast surrogate leverage weighted sampling st...

16 Fanghui Liu, et al. ∙

research

∙ 10/07/2019

Deep Kernel Learning via Random Fourier Features

Kernel learning methods are among the most effective learning methods an...

0 Jiaxuan Xie, et al. ∙

research

∙ 08/19/2019

Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior

Robustness of deep learning methods for limited angle tomography is chal...

0 Yixing Huang, et al. ∙

research

∙ 07/23/2019

A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing

Image smoothing is a fundamental procedure in applications of both compu...

2 Wei Liu, et al. ∙

Xiaolin Huang

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