Jonathan J. Hunt

Reinforcement Learning · 2015–2022

A researcher in the Science corpus: 8 papers; trusts 142 researchers and is trusted by 443 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 168 citations into it; #170 of 2,242 by trust here, #84 by citations
• Artificial Intelligence — 149 citations into it; #873 of 35,345 by trust here, #825 by citations
• Computer Vision and Pattern Recognition — 41 citations into it; #20,939 of 21,575 by trust here, #10,154 by citations
• Control and Systems Engineering — 40 citations into it; #207 of 8,257 by trust here, #17 by citations
• Computational Theory and Mathematics — 26 citations into it; #325 of 12,182 by trust here, #12 by citations
• Cognitive Neuroscience — 21 citations into it
• Management Science and Operations Research — 20 citations into it
• Robotics & Control — 16 citations into it

Whom Jonathan J. Hunt trusts overall

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

1. Geoffrey E. Hinton
2. Richard S. Sutton
3. Diederik P. Kingma
4. Alex Graves
5. Yoshua Bengio
6. J. Urgen Schmidhuber
7. Alex Krizhevsky
8. Sergey Levine
9. Ilya Sutskever
10. Yann LeCun

Who trusts Jonathan J. Hunt overall

1. Sergey Levine
2. Pieter Abbeel
3. Ofir Nachum
4. Shixiang Shane Gu
5. Yoshua Bengio
6. Chelsea Finn
7. Lei Tai
8. Ruslan Salakhutdinov
9. Trevor Darrell
10. Abhinav Kumar Gupta

Papers from Jonathan J. Hunt's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Continuous control with deep reinforcement learning (2015) — 15,687 citations
• Successor Features for Transfer in Reinforcement Learning (2016) — 725 citations
• Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes (2016) — 171 citations
• Memory-based control with recurrent neural networks (2015) — 333 citations
• Deep Reinforcement Learning in Large Discrete Action Spaces (2015) — 640 citations
• Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning (2022) — 677 citations
• Physically Embedded Planning Problems: New Challenges for Reinforcement Learning (2020) — 14 citations
• The Option Keyboard: Combining Skills in Reinforcement Learning (2021) — 116 citations