Jonathan Sorg

Reinforcement Learning · 2010

A researcher in the Science corpus: 5 papers; trusts 27 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 48 citations into it; #1,501 of 2,242 by trust here, #1,190 by citations
• Artificial Intelligence — 28 citations into it; #9,958 of 35,345 by trust here, #10,667 by citations
• Management Science and Operations Research — 10 citations into it; #3,426 of 11,946 by trust here, #5,787 by citations
• Cognitive Science & Psychology — 7 citations into it
• Computational Theory and Mathematics — 7 citations into it
• Statistical Learning Theory — 7 citations into it

Whom Jonathan Sorg trusts overall

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

1. Richard S. Sutton
2. Andrew Y. Ng
3. Andrew Barto
4. Satinder Singh
5. Alexander L. Strehl
6. J. Kolter
7. Michael L. Littman
8. Michael Kearns
9. J. Urgen Schmidhuber
10. Ronen I. Brafman

Who trusts Jonathan Sorg overall

1. Satinder Singh
2. Brendan O'Donoghue
3. Xiaoxiao Guo
4. Kenji Kawaguchi
5. Anuj Mahajan
6. Daniel Jaymin Mankowitz
7. Zeyu Zheng
8. Pierre-Luc Bacon
9. Erik Talvitie
10. Richard L. Lewis

Papers from Jonathan Sorg's perspective

Ordered by how strongly the researchers Jonathan Sorg 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. Reinforcement Learning: An Introduction
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
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

• Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective (2010) — 456 citations
• Variance-Based Rewards for Approximate Bayesian Reinforcement Learning (2010) — 72 citations
• Linear options (2010) — 37 citations
• Reward Design via Online Gradient Ascent (2010) — 113 citations
• Internal Rewards Mitigate Agent Boundedness (2010) — 61 citations