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Liqun Chen

23 papers hereh-index 192.4k citations40 works total

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

author position
  • first author5
  • middle author15
  • last author2

Across the 22 of 23 papers where every author was matched, so the position is known.

fields
  • cs.CL8
  • cs.CV5
  • cs.LG5
  • stat.ML4
  • cs.AI1
same name
  • Liqun Chen — 6 papers, h 1
  • Liqun Chen — 3 papers, h 37
  • Liqun Chen — 3 papers, h 8
  • Liqun Chen — 2 papers
  • Liqun Chen — 2 papers, h 3
  • Liqun Chen — 2 papers, 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
20172026
most citedTriangle Generative Adversarial Networks

78 citations · 311 across the 16 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.ML2018

A Unified Particle-Optimization Framework for Scalable Bayesian Sampling

Changyou Chen, Ruiyi Zhang, Wenlin Wang +2

There has been recent interest in developing scalable Bayesian sampling methods such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) for big-dat…

stat.ML2017★ 17 cited

Symmetric Variational Autoencoder and Connections to Adversarial Learning

Liqun Chen, Shuyang Dai, Yunchen Pu +3

A new form of the variational autoencoder (VAE) is proposed, based on the symmetric Kullback-Leibler divergence. It is demonstrated that learning of the resulting symmetric VAE (sV…

stat.ML2017★ 75 cited

ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

Chunyuan Li, Hao Liu, Changyou Chen +4

We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we prop…

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