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20182024
most citedInterpreting and Evaluating Neural Network Robustness

11 citations · 15 across the 3 of their papers we have counts for

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Showing 2018Show all

7 papers · 1 filter

cs.CV2018

Distilling Critical Paths in Convolutional Neural Networks

Fuxun Yu, Zhuwei Qin, Xiang Chen

Neural network compression and acceleration are widely demanded currently due to the resource constraints on most deployment targets. In this paper, through analyzing the filter ac…

cs.LG2018

Progressive Weight Pruning of Deep Neural Networks using ADMM

Shaokai Ye, Tianyun Zhang, Kaiqi Zhang +10

Deep neural networks (DNNs) although achieving human-level performance in many domains, have very large model size that hinders their broader applications on edge computing devices…

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…