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Li Shen

11 papers here

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

author position
  • middle author8
  • last author1

Across the 9 of 11 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • cs.LG3
  • stat.ME2
  • math.OC1
  • q-bio.NC1
  • q-bio.QM1
ORCID 0000-0002-5443-0503
same name
  • Li Shen — 71 papers, h 29
  • Li Shen — 22 papers, h 12
  • Li Shen — 15 papers, h 15
  • Li Shen — 12 papers, h 8
  • Li Shen — 12 papers, h 4
  • Li Shen — 12 papers, h 6

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 citedThe Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

34 citations · 63 across the 11 of their papers we have counts for

collaborators
Showing cs.LGShow all

3 papers · 1 filter

cs.LG2022★ 1 cited

Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph

Liang Li, Siwei Wang, Xinwang Liu +4

Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proved to be of…

cs.LG2022

Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels

Chuang Zhang, Li Shen, Jian Yang +1

The memorization effect of deep neural networks (DNNs) plays a pivotal role in recent label noise learning methods. To exploit this effect, the model prediction-based methods have…

cs.LG2022★ 34 cited

The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

Shiwei Liu, Tianlong Chen, Xiaohan Chen +4

Random pruning is arguably the most naive way to attain sparsity in neural networks, but has been deemed uncompetitive by either post-training pruning or sparse training. In this p…

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