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Chen-Yu Lee

25 papers hereh-index 2211.2k citations37 works total

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

author position
  • first author9
  • middle author15
  • last author1

Across the 25 of 25 papers where every author was matched, so the position is known.

fields
  • cs.CV11
  • cs.CL5
  • cs.LG5
  • stat.ML2
  • cs.AI1
  • cs.CR1
same name
  • Chen-Yu Lee — 27 papers, h 18
  • Chen-Yu Lee — 4 papers, h 4
  • Chen-Yu Lee — 3 papers, h 2
  • Chen-Yu Lee — 2 papers, h 8
  • Chen-Yu Lee — 1 paper
  • Chen-Yu Lee — 1 paper, h 0

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
20142023
most citedDeeply-Supervised Nets

1k citations · 2.3k across the 24 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.CV2022

Prefix Conditioning Unifies Language and Label Supervision

Kuniaki Saito, Kihyuk Sohn, Xiang Zhang +4

Image-classification datasets have been used to pretrain image recognition models. Recently, web-scale image-caption datasets have emerged as a source of powerful pretraining alter…

cs.LG2022★ 18 cited

DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning

Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi +8

Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store pa…

cs.CL2022

FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction

Chen-Yu Lee, Chun-Liang Li, Timothy Dozat +7

Sequence modeling has demonstrated state-of-the-art performance on natural language and document understanding tasks. However, it is challenging to correctly serialize tokens in fo…

cs.LG2022★ 1 cited

Towards Group Robustness in the presence of Partial Group Labels

Vishnu Suresh Lokhande, Kihyuk Sohn, Jinsung Yoon +3

Learning invariant representations is an important requirement when training machine learning models that are driven by spurious correlations in the datasets. These spurious correl…

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