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Yu Yang

University of California - Los Angeles

9 papers hereh-index 15975 citations27 works total

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

author position
  • first author2
  • middle author5
  • last author1

Across the 8 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.LG4
  • stat.ML1
affiliations
  • University of California - Los Angeles
Homepage
same name
  • Yu Yang — 10 papers, h 3
  • Yu Yang — 8 papers, h 12
  • Yu Yang — 7 papers, h 5
  • Yu Yang — 6 papers, h 17
  • Yu Yang — 5 papers
  • Yu Yang — 5 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
20182022
most citedExplaining AlphaGo: Interpreting Contextual Effects in Neural Networks

3 citations · 11 across the 6 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2022★ 2 cited

Not All Poisons are Created Equal: Robust Training against Data Poisoning

Yu Yang, Tian Yu Liu, Baharan Mirzasoleiman

Data poisoning causes misclassification of test time target examples by injecting maliciously crafted samples in the training data. Existing defenses are often effective only again…

cs.LG2019★ 1 cited

Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract)

Quanshi Zhang, Yu Yang, Ying Nian Wu

This paper presents an unsupervised method to learn a neural network, namely an explainer, to interpret a pre-trained convolutional neural network (CNN), i.e., the explainer uses i…

cs.LG2019

Network Transplanting (extended abstract)

Quanshi Zhang, Yu Yang, Qian Yu +1

This paper focuses on a new task, i.e., transplanting a category-and-task-specific neural network to a generic, modular network without strong supervision. We design a functionally…

cs.LG2018

Network Transplanting

Quanshi Zhang, Yu Yang, Qian Yu +1

This paper focuses on a new task, i.e., transplanting a category-and-task-specific neural network to a generic, modular network without strong supervision. We design an functionall…

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