Kai Fan

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 128 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #25,584 of 35,345 by trust here, #12,761 by citations
• Computer Vision and Pattern Recognition — 56 citations into it; #15,691 of 21,575 by trust here, #9,722 by citations
• Generative Models — 28 citations into it
• Probabilistic & Bayesian Methods — 20 citations into it
• Statistics and Probability — 18 citations into it
• Natural Language Processing — 16 citations into it; #4,225 of 5,688 by trust here, #3,014 by citations
• Optimization & Training — 11 citations into it
• Statistical and Nonlinear Physics — 8 citations into it

Whom Kai Fan trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Ronald J. Williams
4. Yoshua Bengio
5. Ilya Sutskever
6. Alex Krizhevsky
7. Yann LeCun
8. Alex Graves
9. Jerome H. Friedman
10. Ruslan Salakhutdinov

Who trusts Kai Fan overall

1. Chunyuan Li
2. Sergey I. Nikolenko
3. Yizhe Zhang

Papers from Kai Fan's perspective

Ordered by how strongly the researchers Kai Fan 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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

• Adversarial Feature Matching for Text Generation (2017) — 363 citations
• Boosting Variational Inference (2016) — 81 citations
• Zero-Shot Learning via Class-Conditioned Deep Generative Models (2017) — 154 citations