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Shiwei Liu

4 papers here

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

author position
  • first author1
  • middle author2
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • cs.CV1
  • cs.NE1
ORCID 0000-0003-0976-7163
same name
  • Shiwei Liu — 9 papers, h 20
  • Shiwei Liu — 3 papers
  • Shiwei Liu — 3 papers
  • Shiwei Liu — 2 papers, h 4

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

most citedSparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers

6 citations · 13 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2023★ 1 cited

Are Large Kernels Better Teachers than Transformers for ConvNets?

Tianjin Huang, Lu Yin, Zhenyu Zhang +5

This paper reveals a new appeal of the recently emerged large-kernel Convolutional Neural Networks (ConvNets): as the teacher in Knowledge Distillation (KD) for small-kernel ConvNe…

cs.NE2023★ 5 cited

Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks

Zahra Atashgahi, Xuhao Zhang, Neil Kichler +5

Feature selection that selects an informative subset of variables from data not only enhances the model interpretability and performance but also alleviates the resource demands. R…

cs.LG2023★ 1 cited

Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!

Shiwei Liu, Tianlong Chen, Zhenyu Zhang +4

Sparse Neural Networks (SNNs) have received voluminous attention predominantly due to growing computational and memory footprints of consistently exploding parameter count in large…

cs.LG2023★ 6 cited

Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers

Tianlong Chen, Zhenyu Zhang, Ajay Jaiswal +2

Despite their remarkable achievement, gigantic transformers encounter significant drawbacks, including exorbitant computational and memory footprints during training, as well as se…

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