619 citations · 6.4k across the 178 of their papers we have counts for
7 papers · 1 filter
Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric
Yujun Shi, Benben Liao, Guangyong Chen +3
The vulnerability to slight input perturbations is a worrying yet intriguing property of deep neural networks (DNNs). Despite many previous works studying the reason behind such ad…
Exact Low Tubal Rank Tensor Recovery from Gaussian Measurements
Canyi Lu, Jiashi Feng, Zhouchen Lin +1
The recent proposed Tensor Nuclear Norm (TNN) [Lu et al., 2016; 2018a] is an interesting convex penalty induced by the tensor SVD [Kilmer and Martin, 2011]. It plays a similar role…
Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm
Canyi Lu, Jiashi Feng, Yudong Chen +3
In this paper, we consider the Tensor Robust Principal Component Analysis (TRPCA) problem, which aims to exactly recover the low-rank and sparse components from their sum. Our mode…
Discriminative Similarity for Clustering and Semi-Supervised Learning
Yingzhen Yang, Feng Liang, Nebojsa Jojic +3
Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise…
A Unified Framework for Stochastic Matrix Factorization via Variance Reduction
Renbo Zhao, William B. Haskell, Jiashi Feng
We propose a unified framework to speed up the existing stochastic matrix factorization (SMF) algorithms via variance reduction. Our framework is general and it subsumes several we…
The Landscape of Deep Learning Algorithms
Pan Zhou, Jiashi Feng
This paper studies the landscape of empirical risk of deep neural networks by theoretically analyzing its convergence behavior to the population risk as well as its stationary poin…
Distributed Robust Learning
Jiashi Feng, Huan Xu, Shie Mannor
We propose a framework for distributed robust statistical learning on {\em big contaminated data}. The Distributed Robust Learning (DRL) framework can reduce the computational time…