John Asmuth

Artificial Intelligence · 2009

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 6 citations into it; #19,531 of 35,345 by trust here, #14,438 by citations
• Reinforcement Learning — 5 citations into it; #1,330 of 2,242 by trust here, #1,300 by citations

Whom John Asmuth trusts overall

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

1. Tong Zhang
2. Sham M. Kakade
3. Malcolm J. A. Strens
4. Ronen I. Brafman
5. Aaron Wilson
6. Noah D. Goodman
7. Moshe Tennenholtz
8. Michael Kearns
9. Alan Fern

Who trusts John Asmuth overall

1. Peter S. Dayan
2. Todd Hester
3. Lihong Li
4. Michael L. Littman
5. J. Kolter
6. Marc Peter Deisenroth
7. Shipra Agrawal
8. Arthur Guez
9. Jonathan Sorg
10. Raphael Fonteneau

Papers from John Asmuth's perspective

Ordered by how strongly the researchers John Asmuth 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Bayesian Sampling Approach to Exploration in Reinforcement Learning (2009) — 192 citations