Liqun Chen

Artificial Intelligence · with Li · 2017–2022

A researcher in the Science corpus: 9 papers; trusts 527 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 227 citations into it; #16,708 of 35,345 by trust here, #9,092 by citations
• Computer Vision and Pattern Recognition — 224 citations into it; #10,608 of 21,575 by trust here, #7,027 by citations
• Generative Models — 115 citations into it; #917 of 1,956 by trust here, #587 by citations
• Natural Language Processing — 63 citations into it
• Representation & Self-Supervised Learning — 63 citations into it
• Probabilistic & Bayesian Methods — 52 citations into it; #1,392 of 1,525 by trust here, #991 by citations
• Multimodal Learning — 48 citations into it
• Statistical and Nonlinear Physics — 29 citations into it

Whom Liqun Chen trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Kaiming He
6. Yann LeCun
7. Ilya Sutskever
8. Radford M. Neal
9. Ruslan Salakhutdinov
10. Ross B. Girshick

Who trusts Liqun Chen overall

1. Fei Ye
2. Chunyuan Li
3. Yunchen Pu
4. Adrian G. Bors
5. Tian Han
6. Yibo Yang
7. Liunian Harold Li
8. Yuxiang Zhang
9. Zhe Gan
10. Garrett Wilson

Papers from Liqun Chen's perspective

Ordered by how strongly the researchers Liqun Chen trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Symmetric Variational Autoencoder and Connections to Adversarial Learning (2017) — 73 citations
• Triangle Generative Adversarial Networks (2017) — 142 citations
• Contextualized Perturbation for Textual Adversarial Attack (2020) — 285 citations
• Vision-Language Pre-Training with Triple Contrastive Learning (2022) — 392 citations
• Adversarial Symmetric Variational Autoencoder (2017) — 81 citations
• Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE (2021) — 33 citations
• Graph Optimal Transport for Cross-Domain Alignment (2020) — 206 citations
• On Fenchel Mini-Max Learning (2019) — 16 citations
• ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching (2017) — 227 citations