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08/30/2023
Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs
Multitask learning (MTL) leverages task-relatedness to enhance performan...
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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...
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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 ...
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02/23/2023
Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective
Although fast adversarial training provides an efficient approach for bu...
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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...
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11/27/2022
Dynamics of Fecal Coliform Bacteria along Canada's Coast
The vast coastline provides Canada with a flourishing seafood industry i...
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11/22/2022
Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors
As data become increasingly vital for deep learning, a company would be ...
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11/21/2022
Efficient Generalization Improvement Guided by Random Weight Perturbation
To fully uncover the great potential of deep neural networks (DNNs), var...
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09/27/2022
FG-UAP: Feature-Gathering Universal Adversarial Perturbation
Deep Neural Networks (DNNs) are susceptible to elaborately designed pert...
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09/18/2022
Random Fourier Features for Asymmetric Kernels
The random Fourier features (RFFs) method is a powerful and popular tech...
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08/12/2022
Unifying Gradients to Improve Real-world Robustness for Deep Networks
The wide application of deep neural networks (DNNs) demands an increasin...
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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...
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06/18/2022
Piecewise Linear Neural Networks and Deep Learning
As a powerful modelling method, PieceWise Linear Neural Networks (PWLNNs...
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05/26/2022
Trainable Weight Averaging for Fast Convergence and Better Generalization
Stochastic gradient descent (SGD) and its variants are commonly consider...
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05/24/2022
One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks
Unlearnable examples (ULEs) aim to protect data from unauthorized usage ...
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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...
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02/03/2022
Learning with Asymmetric Kernels: Least Squares and Feature Interpretation
Asymmetric kernels naturally exist in real life, e.g., for conditional p...
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11/24/2021
Subspace Adversarial Training
Single-step adversarial training (AT) has received wide attention as it ...
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09/30/2021
Semi-tensor Product-based TensorDecomposition for Neural Network Compression
The existing tensor networks adopt conventional matrix product for conne...
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05/31/2021
Adaptive Feature Alignment for Adversarial Training
Recent studies reveal that Convolutional Neural Networks (CNNs) are typi...
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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...
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05/31/2021
QueryNet: An Efficient Attack Framework with Surrogates Carrying Multiple Identities
Deep Neural Networks (DNNs) are acknowledged as vulnerable to adversaria...
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05/02/2021
Residual Enhanced Multi-Hypergraph Neural Network
Hypergraphs are a generalized data structure of graphs to model higher-o...
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04/13/2021
Towards Unbiased Random Features with Lower Variance For Stationary Indefinite Kernels
Random Fourier Features (RFF) demonstrate wellappreciated performance in...
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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...
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03/20/2021
Train Deep Neural Networks in 40-D Subspaces
Although there are massive parameters in deep neural networks, the train...
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02/20/2021
Going Far Boosts Attack Transferability, but Do Not Do It
Deep Neural Networks (DNNs) could be easily fooled by Adversarial Exampl...
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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...
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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 ...
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10/23/2020
Learn Robust Features via Orthogonal Multi-Path
It is now widely known that by adversarial attacks, clean images with in...
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10/22/2020
One-shot Distributed Algorithm for Generalized Eigenvalue Problem
Nowadays, more and more datasets are stored in a distributed way for the...
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09/10/2020
End-to-end Kernel Learning via Generative Random Fourier Features
Random Fourier features enable researchers to build feature map to learn...
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08/16/2020
Attack on Multi-Node Attention for Object Detection
This paper focuses on high-transferable adversarial attacks on detection...
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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...
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05/30/2020
Generalizing Random Fourier Features via Generalized Measures
We generalize random Fourier features, that usually require kernel funct...
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05/06/2020
A Communication-Efficient Distributed Algorithm for Kernel Principal Component Analysis
Principal Component Analysis (PCA) is a fundamental technology in machin...
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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...
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04/23/2020
Sparse Generalized Canonical Correlation Analysis: Distributed Alternating Iteration based Approach
Sparse canonical correlation analysis (CCA) is a useful statistical tool...
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03/19/2020
Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention
With the popularity of stereo cameras in computer assisted surgery techn...
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03/17/2020
Real-time Image Smoothing via Iterative Least Squares
Edge-preserving image smoothing is a fundamental procedure for many comp...
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03/04/2020
Double Backpropagation for Training Autoencoders against Adversarial Attack
Deep learning, as widely known, is vulnerable to adversarial samples. Th...
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03/04/2020
Type I Attack for Generative Models
Generative models are popular tools with a wide range of applications. N...
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01/21/2020
Generate High-Resolution Adversarial Samples by Identifying Effective Features
As the prevalence of deep learning in computer vision, adversarial sampl...
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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...
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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...
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12/16/2019
DAmageNet: A Universal Adversarial Dataset
It is now well known that deep neural networks (DNNs) are vulnerable to ...
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11/20/2019
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling
In this paper, we propose a fast surrogate leverage weighted sampling st...
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10/07/2019
Deep Kernel Learning via Random Fourier Features
Kernel learning methods are among the most effective learning methods an...
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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...
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07/23/2019
