Harr Chen

Artificial Intelligence · 2006–2010

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 26 citations into it
• Natural Language Processing — 13 citations into it; #1,698 of 5,688 by trust here, #1,926 by citations
• Reinforcement Learning — 5 citations into it; #792 of 2,242 by trust here, #773 by citations

Whom Harr Chen trusts overall

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

1. David D. Lewis
2. Richard S. Sutton
3. Chengxiang Zhai
4. Michael Collins
5. Daniel Klein
6. Percy Liang
7. John D. Lafferty
8. Stephen E. Robertson
9. Jade Goldstein-Stewart
10. Michael I. Jordan

Who trusts Harr Chen overall

1. Filip Radlinski
2. Sreenivas Gollapudi
3. Percy Liang
4. Ashwin Deepak Swaminathan
5. Sahisnu Mazumder
6. Thorsten Joachims
7. Yoav Artzi
8. S. Branavan
9. C. Clarke
10. Jacob Andreas

Papers from Harr Chen's perspective

Ordered by how strongly the researchers Harr 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. 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Using Universal Linguistic Knowledge to Guide Grammar Induction (2010) — 149 citations
• Reinforcement Learning for Mapping Instructions to Actions (2009) — 333 citations
• Less is More Probabilistic Models for Retrieving Fewer Relevant Documents (2006)