Kai Chen

Artificial Intelligence · with Corrado · 2011–2018

A researcher in the Science corpus: 5 papers; trusts 88 researchers and is trusted by 4,313 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 77 citations into it; #95 of 35,345 by trust here, #97 by citations
• Natural Language Processing — 71 citations into it; #39 of 5,688 by trust here, #30 by citations
• Computer Vision and Pattern Recognition — 59 citations into it; #1,565 of 21,575 by trust here, #1,244 by citations
• Representation & Self-Supervised Learning — 25 citations into it; #15 of 3,145 by trust here, #9 by citations
• Optimization & Training — 24 citations into it; #1,015 of 2,601 by trust here, #135 by citations
• Large Language Models — 20 citations into it
• Model Architectures — 18 citations into it; #1,172 of 2,278 by trust here, #502 by citations
• Evaluation & Benchmarks — 8 citations into it

Whom Kai Chen trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Tomas Mikolov
4. Yann LeCun
5. Andriy Mnih
6. Alex Krizhevsky
7. Richard Socher
8. Andrew Y. Ng
9. Diederik P. Kingma
10. Ronan Collobert

Who trusts Kai Chen overall

1. Yoshua Bengio
2. Mohit Bansal
3. Yoav Goldberg
4. Kyunghyun Cho
5. Quoc V. Le
6. Zhe Gan
7. Ruslan Salakhutdinov
8. Jianfeng Gao
9. Percy Liang
10. Trevor Darrell

Papers from Kai Chen's perspective

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

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

• Distributed Representations of Words and Phrases and their Compositionality (2013) — 35,459 citations
• Efficient Estimation of Word Representations in Vector Space (2013) — 34,723 citations
• Building high-level features using large scale unsupervised learning (2011) — 2,318 citations
• QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension (2018) — 1,133 citations
• Large Scale Distributed Deep Networks (2012) — 4,179 citations