
Adaptive Feature Alignment for Adversarial Training
Recent studies reveal that Convolutional Neural Networks (CNNs) are typi...
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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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QueryNet: An Efficient Attack Framework with Surrogates Carrying Multiple Identities
Deep Neural Networks (DNNs) are acknowledged as vulnerable to adversaria...
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Residual Enhanced MultiHypergraph Neural Network
Hypergraphs are a generalized data structure of graphs to model highero...
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Towards Unbiased Random Features with Lower Variance For Stationary Indefinite Kernels
Random Fourier Features (RFF) demonstrate wellappreciated performance in...
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Weighted Neural Tangent Kernel: A Generalized and Improved NetworkInduced Kernel
The Neural Tangent Kernel (NTK) has recently attracted intense study, as...
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Train Deep Neural Networks in 40D Subspaces
Although there are massive parameters in deep neural networks, the train...
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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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Learning TubuleSensitive CNNs for Pulmonary Airway and ArteryVein Segmentation in CT
Training convolutional neural networks (CNNs) for segmentation of pulmon...
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Towards a Unified Quadrature Framework for LargeScale Kernel Machines
In this paper, we develop a quadrature framework for largescale kernel ...
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Learn Robust Features via Orthogonal MultiPath
It is now widely known that by adversarial attacks, clean images with in...
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Oneshot Distributed Algorithm for Generalized Eigenvalue Problem
Nowadays, more and more datasets are stored in a distributed way for the...
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Endtoend Kernel Learning via Generative Random Fourier Features
Random Fourier features enable researchers to build feature map to learn...
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Attack on MultiNode Attention for Object Detection
This paper focuses on hightransferable adversarial attacks on detection...
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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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Generalizing Random Fourier Features via Generalized Measures
We generalize random Fourier features, that usually require kernel funct...
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A CommunicationEfficient Distributed Algorithm for Kernel Principal Component Analysis
Principal Component Analysis (PCA) is a fundamental technology in machin...
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Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond
Random features is one of the most soughtafter research topics in stati...
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Sparse Generalized Canonical Correlation Analysis: Distributed Alternating Iteration based Approach
Sparse canonical correlation analysis (CCA) is a useful statistical tool...
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Stereo Endoscopic Image SuperResolution Using DisparityConstrained Parallel Attention
With the popularity of stereo cameras in computer assisted surgery techn...
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Realtime Image Smoothing via Iterative Least Squares
Edgepreserving image smoothing is a fundamental procedure for many comp...
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Double Backpropagation for Training Autoencoders against Adversarial Attack
Deep learning, as widely known, is vulnerable to adversarial samples. Th...
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Type I Attack for Generative Models
Generative models are popular tools with a wide range of applications. N...
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Generate HighResolution Adversarial Samples by Identifying Effective Features
As the prevalence of deep learning in computer vision, adversarial sampl...
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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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MixedPrecision 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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DAmageNet: A Universal Adversarial Dataset
It is now well known that deep neural networks (DNNs) are vulnerable to ...
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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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Deep Kernel Learning via Random Fourier Features
Kernel learning methods are among the most effective learning methods an...
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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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A Generalized Framework for Edgepreserving and Structurepreserving Image Smoothing
Image smoothing is a fundamental procedure in applications of both compu...
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AirwayNet: A VoxelConnectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks
Airway segmentation on CT scans is critical for pulmonary disease diagno...
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Robust Visual Tracking Revisited: From Correlation Filter to Template Matching
In this paper, we propose a novel matching based tracker by investigatin...
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Online PCB Defect Detector On A New PCB Defect Dataset
Previous works for PCB defect detection based on image difference and im...
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Embedding Bilateral Filter in Least Squares for Efficient Edgepreserving Image Smoothing
Edgepreserving smoothing is a fundamental procedure for many computer v...
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VarifocalNet: A Chromosome Classification Approach using Deep Convolutional Networks
Chromosome classification is critical for karyotyping in abnormality dia...
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Generalization Properties of hyperRKHS and its Application to OutofSample Extensions
Hyperkernels endowed by hyperReproducing Kernel Hilbert Space (hyperR...
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Fast Signal Recovery from Saturated Measurements by Linear Loss and Nonconvex Penalties
Sign information is the key to overcoming the inevitable saturation erro...
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Adversarial Attack Type I: Generating False Positives
False positive and false negative rates are equally important for evalua...
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Learning Dataadaptive Nonparametric Kernels
Traditional kernels or their combinations are often not sufficiently fle...
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EdgePreserving Piecewise Linear Image Smoothing Using Piecewise Constant Filters
Most image smoothing filters in the literature assume a piecewise consta...
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ScaleSpace Anisotropic Total Variation for Limited Angle Tomography
This paper addresses streak reduction in limited angle tomography. Altho...
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Indefinite Kernel Logistic Regression
Traditionally, kernel learning methods requires positive definitiveness ...
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Nonconvex penalties with analytical solutions for onebit compressive sensing
Onebit measurements widely exist in the real world, and they can be use...
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Online Robust Principal Component Analysis with Change Point Detection
Robust PCA methods are typically batch algorithms which requires loading...
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Mixed onebit compressive sensing with applications to overexposure correction for CT reconstruction
When a measurement falls outside the quantization or measurable range, i...
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Pinball Loss Minimization for Onebit Compressive Sensing
The onebit quantization can be implemented by one single comparator, wh...
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Xiaolin Huang
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