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Jian Tang

23 papers hereh-index 334.8k citations60 works total

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

author position
  • middle author18
  • last author3

Across the 21 of 23 papers where every author was matched, so the position is known.

fields
  • cs.LG10
  • cs.CV5
  • cs.NE3
  • cs.DC2
  • cs.AI1
  • cs.IT1
same name
  • Jian Tang — 72 papers, h 45
  • Jian Tang — 41 papers, h 20
  • Jian Tang — 26 papers, h 12
  • Jian Tang — 19 papers, h 19
  • Jian Tang — 18 papers, h 13
  • Jian Tang — 16 papers, h 5

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
20172022
most citedA Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers

498 citations · 1.1k across the 23 of their papers we have counts for

collaborators
Showing 2019Show all

3 papers · 1 filter

cs.LG2019★ 24 cited

PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-time Execution on Mobile Devices

Xiaolong Ma, Fu-Ming Guo, Wei Niu +5

Model compression techniques on Deep Neural Network (DNN) have been widely acknowledged as an effective way to achieve acceleration on a variety of platforms, and DNN weight prunin…

cs.LG2019★ 35 cited

AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates

Ning Liu, Xiaolong Ma, Zhiyuan Xu +3

Structured weight pruning is a representative model compression technique of DNNs to reduce the storage and computation requirements and accelerate inference. An automatic hyperpar…

cs.NE2019★ 26 cited

Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM

Shaokai Ye, Xiaoyu Feng, Tianyun Zhang +11

Weight pruning and weight quantization are two important categories of DNN model compression. Prior work on these techniques are mainly based on heuristics. A recent work developed…

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