Haochen Liu

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 5 papers; trusts 246 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 212 citations into it; #11,546 of 35,345 by trust here, #8,439 by citations
• Safety, Robustness & Interpretability — 139 citations into it; #799 of 2,251 by trust here, #737 by citations
• Natural Language Processing — 99 citations into it; #2,312 of 5,688 by trust here, #2,203 by citations
• Computer Vision and Pattern Recognition — 61 citations into it; #17,240 of 21,575 by trust here, #11,286 by citations
• Representation & Self-Supervised Learning — 15 citations into it
• Model Architectures — 10 citations into it
• Optimization & Training — 10 citations into it
• Reinforcement Learning — 9 citations into it

Whom Haochen Liu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Yoshua Bengio
5. Ronald J. Williams
6. Oriol Vinyals
7. Alex Krizhevsky
8. Jimmy Ba
9. Yann LeCun
10. Sepp Hochreiter

Who trusts Haochen Liu overall

1. Emily Dinan
2. Eric Michael Smith
3. Emily Sheng
4. Adina Williams
5. Jiaxu Zhao
6. Gavin Abercrombie
7. A. S. Bergman
8. Kai-Wei Chang
9. Soumya Barikeri
10. Dian Yu

Papers from Haochen Liu's perspective

Ordered by how strongly the researchers Haochen Liu 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Does Gender Matter? Towards Fairness in Dialogue Systems (2019) — 156 citations
• Adversarial Attacks and Defenses in Images, Graphs and Text: A Review (2019) — 757 citations
• Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Models (2020) — 15 citations
• Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning (2020) — 79 citations
• Say What I Want: Towards the Dark Side of Neural Dialogue Models (2019) — 17 citations