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Xiang Chen

14 papers hereh-index 161.4k citations44 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author9

Across the 10 of 14 papers where every author was matched, so the position is known.

fields
  • cs.LG6
  • cs.CV4
  • cs.CR2
  • cs.AR1
  • cs.DC1
same name
  • Xiang Chen — 19 papers
  • Xiang Chen — 12 papers
  • Xiang Chen — 9 papers
  • Xiang Chen — 8 papers, h 16
  • Xiang Chen — 8 papers
  • Xiang Chen — 6 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182020
most citedInterpreting and Evaluating Neural Network Robustness

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

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2020

AntiDote: Attention-based Dynamic Optimization for Neural Network Runtime Efficiency

Fuxun Yu, Chenchen Liu, Di Wang +2

Convolutional Neural Networks (CNNs) achieved great cognitive performance at the expense of considerable computation load. To relieve the computation load, many optimization works…

cs.CV2020

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

Xiaolong Ma, Wei Niu, Tianyun Zhang +8

Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on vario…

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.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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.