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researcher

Zhe Gan

Microsoft

76 papers hereh-index 7424k citations157 works total

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

author position
  • first author5
  • middle author68
  • last author1

Across the 74 of 76 papers where every author was matched, so the position is known.

fields
  • cs.CV32
  • cs.CL28
  • cs.LG10
  • stat.ML4
  • astro-ph.HE1
  • eess.AS1
affiliations
  • Microsoft
Homepage
same name
  • Zhe Gan — 25 papers, h 18
  • Zhe Gan — 11 papers, h 7
  • Zhe Gan — 6 papers
  • Zhe Gan — 5 papers, h 3
  • Zhe Gan — 1 paper, h 1

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
20162023
most citedAttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

158 citations · 1.1k across the 40 of their papers we have counts for

collaborators
Showing stat.MLShow all

4 papers · 1 filter

stat.ML2021★ 9 cited

Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE

Junya Chen, Zhe Gan, Xuan Li +10

InfoNCE-based contrastive representation learners, such as SimCLR, have been tremendously successful in recent years. However, these contrastive schemes are notoriously resource de…

stat.ML2017★ 124 cited

Adversarial Feature Matching for Text Generation

Yizhe Zhang, Zhe Gan, Kai Fan +4

The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with…

stat.ML2017★ 6 cited

Stochastic Gradient Monomial Gamma Sampler

Yizhe Zhang, Changyou Chen, Zhe Gan +2

Recent advances in stochastic gradient techniques have made it possible to estimate posterior distributions from large datasets via Markov Chain Monte Carlo (MCMC). However, when t…

stat.ML2016

Factored Temporal Sigmoid Belief Networks for Sequence Learning

Jiaming Song, Zhe Gan, Lawrence Carin

Deep conditional generative models are developed to simultaneously learn the temporal dependencies of multiple sequences. The model is designed by introducing a three-way weight te…

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