Tom Jin

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 8 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #30,107 of 35,345 by trust here, #23,077 by citations
• Probabilistic & Bayesian Methods — 20 citations into it
• Reinforcement Learning — 11 citations into it
• Generative Models — 7 citations into it; #1,678 of 1,956 by trust here, #1,601 by citations

Whom Tom Jin trusts overall

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

1. Diederik P. Kingma
2. Ronald J. Williams
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Volodymyr Mnih
6. Radford M. Neal
7. Danilo Jimenez Rezende
8. Richard S. Sutton
9. Junyoung Chung
10. Alex Graves

Who trusts Tom Jin overall

1. Frank D. Wood
2. Tuan Anh Le

Papers from Tom Jin's perspective

Ordered by how strongly the researchers Tom Jin 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. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
8. Long Short-Term Memory
9. Asynchronous Methods for Deep Reinforcement Learning
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Auto-Encoding Sequential Monte Carlo (2017) — 166 citations
• Idiosyncrasies and challenges of data driven learning in electronic trading (2018) — 21 citations