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researcher

Yu Lu

8 papers here

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

author position
  • first author2
  • middle author4

Across the 6 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.CL1
  • cs.LG1
  • eess.IV1
  • stat.ML1
ORCID 0000-0001-9108-2619
same name
  • Yu Lu — 38 papers
  • Yu Lu — 10 papers
  • Yu Lu — 10 papers, h 31
  • Yu Lu — 6 papers, h 11
  • Yu Lu — 4 papers, h 12
  • Yu Lu — 3 papers, h 7

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
20142024
most citedIndividualized Rank Aggregation using Nuclear Norm Regularization

11 citations · 18 across the 8 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024

Hierarchical Prompts for Rehearsal-free Continual Learning

Yukun Zuo, Hantao Yao, Lu Yu +2

Continual learning endeavors to equip the model with the capability to integrate current task knowledge while mitigating the forgetting of past task knowledge. Inspired by prompt t…

cs.CV2023★ 2 cited

X-Pruner: eXplainable Pruning for Vision Transformers

Lu Yu, Wei Xiang

Recently vision transformer models have become prominent models for a range of tasks. These models, however, usually suffer from intensive computational costs and heavy memory requ…

cs.CV2022★ 2 cited

eX-ViT: A Novel eXplainable Vision Transformer for Weakly Supervised Semantic Segmentation

Lu Yu, Wei Xiang, Juan Fang +2

Recently vision transformer models have become prominent models for a range of vision tasks. These models, however, are usually opaque with weak feature interpretability. Moreover,…

cs.CV2021★ 2 cited

Continually Learning Self-Supervised Representations with Projected Functional Regularization

Alex Gomez-Villa, Bartlomiej Twardowski, Lu Yu +2

Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches. However, these methods are unable t…

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