Carlos Diuk

Reinforcement Learning · 2007–2014

A researcher in the Science corpus: 5 papers; trusts 25 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 69 citations into it; #593 of 2,242 by trust here, #906 by citations
• Artificial Intelligence — 37 citations into it; #6,206 of 35,345 by trust here, #7,728 by citations
• Cognitive Neuroscience — 18 citations into it
• Statistical Learning Theory — 14 citations into it
• Computational Theory and Mathematics — 11 citations into it
• Management Science and Operations Research — 9 citations into it; #10,187 of 11,946 by trust here, #7,262 by citations

Whom Carlos Diuk trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Thomas G. Dietterich
4. J. Urgen Schmidhuber
5. Michael L. Littman
6. Satinder Singh
7. Craig Boutilier
8. Lihong Li
9. Michael Kearns
10. Sham M. Kakade

Who trusts Carlos Diuk overall

1. Marc G. Bellemare
2. Lihong Li
3. Michael L. Littman
4. Eric Crawford
5. Khimya Khetarpal
6. Joel Veness
7. Thomas J. Walsh
8. Shimon Whiteson
9. Todd Hester
10. Joelle Pineau

Papers from Carlos Diuk's perspective

Ordered by how strongly the researchers Carlos Diuk 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. Reinforcement Learning: An Introduction
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• An object-oriented representation for efficient reinforcement learning (2008) — 355 citations
• Optimal Behavioral Hierarchy (2014) — 173 citations
• Exploring compact reinforcement-learning representations with linear regression (2009) — 120 citations
• Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humans (2013) — 32 citations
• Efficient Structure Learning in Factored-State MDPs (2007) — 144 citations