activity
20182026
most citedInterpreting and Evaluating Neural Network Robustness

11 citations · 16 across the 8 of their papers we have counts for

collaborators
Showing 2018Show all

5 papers · 1 filter

cs.CV2018

Demystifying Neural Network Filter Pruning

Zhuwei Qin, Fuxun Yu, ChenChen Liu +1

Based on filter magnitude ranking (e.g. L1 norm), conventional filter pruning methods for Convolutional Neural Networks (CNNs) have been proved with great effectiveness in computat…

cs.LG2018

Interpreting Adversarial Robustness: A View from Decision Surface in Input Space

Fuxun Yu, Chenchen Liu, Yanzhi Wang +2

One popular hypothesis of neural network generalization is that the flat local minima of loss surface in parameter space leads to good generalization. However, we demonstrate that…

cs.LG2018

Functionality-Oriented Convolutional Filter Pruning

Zhuwei Qin, Fuxun Yu, Chenchen Liu +1

The sophisticated structure of Convolutional Neural Network (CNN) allows for outstanding performance, but at the cost of intensive computation. As significant redundancies inevitab…

cs.CR2018

HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition

Zirui Xu, Fuxun Yu, Chenchen Liu +1

Nowadays, machine learning based Automatic Speech Recognition (ASR) technique has widely spread in smartphones, home devices, and public facilities. As convenient as this technolog…

cs.LG2018

Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients

Fuxun Yu, Zirui Xu, Yanzhi Wang +2

In recent years, neural networks have demonstrated outstanding effectiveness in a large amount of applications.However, recent works have shown that neural networks are susceptible…