M. Stone

Reinforcement Learning · 2016

A researcher in the Science corpus: 2 papers; trusts 43 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 13 citations into it; #880 of 2,242 by trust here, #2,125 by citations
• Artificial Intelligence — 12 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom M. Stone trusts overall

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

1. Richard S. Sutton
2. Nikolaus Hansen
3. Andrew Barto
4. Diederik P. Kingma
5. Marc G. Bellemare
6. Volodymyr Mnih
7. Jimmy Ba
8. Geoffrey E. Hinton
9. Timothy P. Lillicrap
10. Yangqing Jia

Who trusts M. Stone overall

1. A. Srinivas
2. Justin Fu
3. Brendan O'Donoghue
4. Balaraman Ravindran
5. Aviral Kumar
6. R´emi Munos
7. Stefano V. Albrecht
8. Rasool Fakoor
9. Sergey Levine
10. Koray Kavukcuoglu

Papers from M. Stone's perspective

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

Their papers

• On-Policy vs. Off-Policy Updates for Deep Reinforcement Learning (2016) — 30 citations
• Half Field Offense: An Environment for Multiagent Learning and Ad Hoc Teamwork (2016) — 89 citations