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Long Lan

4 papers here

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

author position
  • middle author4

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

fields
  • cs.LG4
ORCID 0000-0002-4238-8985
same name
  • Long Lan — 7 papers, h 6
  • Long Lan — 7 papers, h 8
  • Long Lan — 6 papers, h 3
  • Long Lan — 1 paper, h 5
  • Long Lan — 1 paper, h 2
  • Long Lan — 1 paper, h 8

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
20222024
most citedBilateral Dependency Optimization: Defending Against Model-inversion Attacks

24 citations · 26 across the 4 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2024

Tackling Noisy Labels with Network Parameter Additive Decomposition

Jingyi Wang, Xiaobo Xia, Long Lan +5

Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization. The memorization effect of deep networks shows…

cs.LG2023★ 2 cited

SODA: Robust Training of Test-Time Data Adaptors

Zige Wang, Yonggang Zhang, Zhen Fang +3

Adapting models deployed to test distributions can mitigate the performance degradation caused by distribution shifts. However, privacy concerns may render model parameters inacces…

cs.LG2023

Towards Radar Emitter Recognition in Changing Environments with Domain Generalization

Honglin Wu, Xueqiong Li, Long Lan +2

Analyzing radar signals from complex Electronic Warfare (EW) environment is a non-trivial task.However, in the real world, the changing EW environment results in inconsistent signa…

cs.LG2022★ 24 cited

Bilateral Dependency Optimization: Defending Against Model-inversion Attacks

Xiong Peng, Feng Liu, Jingfen Zhang +4

Through using only a well-trained classifier, model-inversion (MI) attacks can recover the data used for training the classifier, leading to the privacy leakage of the training dat…

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