research
∙
04/14/2023
Generating Adversarial Examples with Better Transferability via Masking Unimportant Parameters of Surrogate Model
Deep neural networks (DNNs) have been shown to be vulnerable to adversar...
share
research
∙
03/07/2023
Computing Effective Resistances on Large Graphs Based on Approximate Inverse of Cholesky Factor
Effective resistance, which originates from the field of circuits analys...
research
∙
06/16/2022
Boosting the Adversarial Transferability of Surrogate Model with Dark Knowledge
Deep neural networks (DNNs) for image classification are known to be vul...
research
∙
06/13/2022
Pursuing More Effective Graph Spectral Sparsifiers via Approximate Trace Reduction
Spectral graph sparsification aims to find ultra-sparse subgraphs which ...
research
∙
10/25/2021
NetMF+: Network Embedding Based on Fast and Effective Single-Pass Randomized Matrix Factorization
In this work, we propose NetMF+, a fast, memory-efficient, scalable, and...
research
∙
07/14/2021
CNN-Cap: Effective Convolutional Neural Network Based Capacitance Models for Full-Chip Parasitic Extraction
Accurate capacitance extraction is becoming more important for designing...
research
∙
12/03/2020
AugSplicing: Synchronized Behavior Detection in Streaming Tensors
How can we track synchronized behavior in a stream of time-stamped tuple...
research
∙
09/04/2020
Efficient Model-Based Collaborative Filtering with Fast Adaptive PCA
A model-based collaborative filtering (CF) approach utilizing fast adapt...
research
∙
07/08/2020
Delving into the Adversarial Robustness on Face Recognition
Face recognition has recently made substantial progress and achieved hig...
research
∙
03/21/2020
DP-Net: Dynamic Programming Guided Deep Neural Network Compression
In this work, we propose an effective scheme (called DP-Net) for compres...
research
∙
01/02/2020
Kernelized Support Tensor Train Machines
Tensor, a multi-dimensional data structure, has been exploited recently ...
research
∙
08/07/2019
Faster Tensor Train Decomposition for Sparse Data
In recent years, the application of tensors has become more widespread i...
research
∙
12/13/2018
Optimal Algorithm for Profiling Dynamic Arrays with Finite Values
How can one quickly answer the most and top popular objects at any time,...
research
∙
10/16/2018
Faster Matrix Completion Using Randomized SVD
Matrix completion is a widely used technique for image inpainting and pe...
research
∙
10/16/2018
Fast Randomized PCA for Sparse Data
Principal component analysis (PCA) is widely used for dimension reductio...
research
∙
07/26/2018
A Unified Approximation Framework for Deep Neural Networks
Deep neural networks (DNNs) have achieved significant success in a varie...
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...
research
∙
04/17/2018
Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains
We propose a new tensor completion method based on tensor trains. The to...
research
∙
04/17/2018
Fast and Accurate Tensor Completion with Tensor Trains: A System Identification Approach
We propose a novel tensor completion approach by equating it to a system...
research
∙
07/25/2017
Computing low-rank approximations of large-scale matrices with the Tensor Network randomized SVD
We propose a new algorithm for the computation of a singular value decom...
research
∙
04/14/2017