Natalia Córdova

Reinforcement Learning · 2013–2014

A researcher in the Science corpus: 2 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 20 citations into it
• Cognitive Neuroscience — 16 citations into it
• Artificial Intelligence — 13 citations into it
• Computational Theory and Mathematics — 6 citations into it

Whom Natalia Córdova trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. J. Urgen Schmidhuber
4. David John Cameron MacKay
5. Thomas G. Dietterich
6. Matthew Botvinick
7. Satinder Singh
8. Sebastian Thrun
9. Doina Precup
10. Luca Scrucca

Papers from Natalia Córdova's perspective

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

1. Reinforcement Learning: An Introduction
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning
6. Long Short-Term Memory
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Information Theory: 1948-1998 - Guest Editorial
9. Feudal Reinforcement Learning
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Optimal Behavioral Hierarchy (2014) — 173 citations
• Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humans (2013) — 32 citations