Joseph Modayil

Reinforcement Learning · 2011–2019

A researcher in the Science corpus: 11 papers; trusts 82 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 174 citations into it; #483 of 2,242 by trust here, #251 by citations
• Artificial Intelligence — 132 citations into it; #4,276 of 35,345 by trust here, #2,401 by citations
• Cognitive Neuroscience — 41 citations into it; #4,287 of 10,852 by trust here, #3,321 by citations
• Computer Vision and Pattern Recognition — 33 citations into it
• Optimization & Training — 33 citations into it
• Management Science and Operations Research — 31 citations into it
• Electrical and Electronic Engineering — 17 citations into it
• Computational Theory and Mathematics — 8 citations into it

Whom Joseph Modayil trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Geoffrey E. Hinton
4. Hamid Reza Maei
5. Leslie Pack Kaelbling
6. Hado van Hasselt
7. Diederik P. Kingma
8. Peter S. Dayan
9. Yoshua Bengio
10. Ronald J. Williams

Who trusts Joseph Modayil overall

1. Richard S. Sutton
2. Adam White
3. Hado van Hasselt
4. Sergey Levine
5. A. Mahmood
6. Danijar Hafner
7. Martha White
8. Matteo Hessel
9. Tom Schaul
10. Yoshua Bengio

Papers from Joseph Modayil's perspective

Ordered by how strongly the researchers Joseph Modayil 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: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Rainbow: Combining Improvements in Deep Reinforcement Learning (2017) — 2,665 citations
• Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction (2011) — 543 citations
• Deep Reinforcement Learning and the Deadly Triad (2018) — 287 citations
• Ray Interference: a Source of Plateaus in Deep Reinforcement Learning (2019) — 79 citations
• Vector-based navigation using grid-like representations in artificial agents (2018) — 716 citations
• Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017 (2017) — 38 citations
• Multi-timescale nexting in a reinforcement learning robot (2011) — 133 citations
• The Barbados 2018 List of Open Issues in Continual Learning (2018) — 12 citations
• On Inductive Biases in Deep Reinforcement Learning (2019) — 47 citations
• Universal Option Models (2014) — 32 citations
• Surprise and Curiosity for Big Data Robotics (2014)