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Minwoo Lee

University of North Carolina, Charlotte

4 papers hereh-index 171.4k citations106 works total

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

author position
  • middle author2
  • last author2

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

fields
  • cs.LG2
  • cs.CV1
  • cs.NI1
affiliations
  • University of North Carolina, Charlotte
HomepageORCID 0000-0002-6860-608X
same name
  • Minwoo Lee — 3 papers
  • Minwoo Lee — 2 papers
  • Minwoo Lee — 1 paper, h 3
  • Minwoo Lee — 1 paper, h 16
  • Minwoo Lee — 1 paper

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 citedLearning from Few Examples: A Summary of Approaches to Few-Shot Learning

141 citations · 144 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CV2022★ 1 cited

GaitMixer: Skeleton-based Gait Representation Learning via Wide-spectrum Multi-axial Mixer

Ekkasit Pinyoanuntapong, Ayman Ali, Pu Wang +2

Most existing gait recognition methods are appearance-based, which rely on the silhouettes extracted from the video data of human walking activities. The less-investigated skeleton…

cs.LG2022★ 141 cited

Learning from Few Examples: A Summary of Approaches to Few-Shot Learning

Archit Parnami, Minwoo Lee

Few-Shot Learning refers to the problem of learning the underlying pattern in the data just from a few training samples. Requiring a large number of data samples, many deep learnin…

cs.NI2021

Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing

Pinyarash Pinyoanuntapong, Tagore Pothuneedi, Ravikumar Balakrishnan +3

Federated Learning (FL) over wireless multi-hop edge computing networks, i.e., multi-hop FL, is a cost-effective distributed on-device deep learning paradigm. This paper presents F…

cs.LG2021★ 2 cited

Demystifying Deep Neural Networks Through Interpretation: A Survey

Giang Dao, Minwoo Lee

Modern deep learning algorithms tend to optimize an objective metric, such as minimize a cross entropy loss on a training dataset, to be able to learn. The problem is that the sing…

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