Jifan Chen

Artificial Intelligence · with Durrett · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 145 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #12,106 of 35,345 by trust here, #12,761 by citations
• Natural Language Processing — 72 citations into it; #2,469 of 5,688 by trust here, #2,732 by citations
• Computer Vision and Pattern Recognition — 46 citations into it; #16,721 of 21,575 by trust here, #14,822 by citations
• Evaluation & Benchmarks — 16 citations into it
• Model Architectures — 16 citations into it; #2,081 of 2,278 by trust here, #2,128 by citations
• Graph Learning — 7 citations into it
• Information Systems — 5 citations into it

Whom Jifan Chen trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Jason Aaron Edward Weston
4. Alex Graves
5. Diederik P. Kingma
6. Oriol Vinyals
7. Ilya Sutskever
8. Richard Socher
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Jifan Chen overall

1. Xuezhi Wang
2. Viktor Schlegel
3. Jiangming Liu
4. Matt Gardner
5. Sewon Min
6. Weiwen Xu
7. Kaj Bostrom
8. Jason Wei
9. Greg Durrett
10. Ethan Perez

Papers from Jifan Chen's perspective

Ordered by how strongly the researchers Jifan 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. Long Short-Term Memory
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Understanding Dataset Design Choices for Multi-hop Reasoning (2019) — 116 citations
• Multi-hop Question Answering via Reasoning Chains (2019) — 88 citations
• Modelling Interaction of Sentence Pair with Coupled-LSTMs (2016) — 48 citations