James Goodman

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 29 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 13 citations into it; #12,864 of 35,345 by trust here, #33,294 by citations
• Reinforcement Learning — 8 citations into it
• Economics and Econometrics — 6 citations into it

Whom James Goodman trusts overall

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

1. Stéphane Ross
2. Bruno Bouzy
3. David Silver
4. Thomas W. Anthony
5. G. Tesauro
6. Jakob N. Foerster
7. Guillaume M
8. Rodrigo Canaan
9. Cameron Browne
10. Drew Bagnell

Who trusts James Goodman overall

1. Hengyuan Hu
2. Jakob N. Foerster
3. Rodrigo Canaan
4. Nolan Bard
5. Julian Togelius
6. Andy Nealen
7. Stefan Menzel

Papers from James Goodman's perspective

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

1. Gradient-based learning applied to document recognition
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
7. Asynchronous Methods for Deep Reinforcement Learning
8. Reinforcement Learning: An Introduction
9. Human-level control through deep reinforcement learning
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Re-determinizing Information Set Monte Carlo Tree Search in Hanabi (2019) — 14 citations