John D. Co-Reyes

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 6 papers; trusts 237 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 156 citations into it; #10,623 of 35,345 by trust here, #8,439 by citations
• Reinforcement Learning — 118 citations into it; #900 of 2,242 by trust here, #778 by citations
• Computer Vision and Pattern Recognition — 79 citations into it
• Representation & Self-Supervised Learning — 29 citations into it
• Control and Systems Engineering — 20 citations into it
• Optimization & Training — 18 citations into it
• Management Science and Operations Research — 17 citations into it; #4,650 of 11,946 by trust here, #3,825 by citations
• Model Architectures — 17 citations into it

Whom John D. Co-Reyes trusts overall

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

1. Sergey Levine
2. J. Urgen Schmidhuber
3. Geoffrey E. Hinton
4. John Schulman
5. Diederik P. Kingma
6. Pieter Abbeel
7. Yoshua Bengio
8. Chelsea Finn
9. Richard S. Sutton
10. Alex Krizhevsky

Who trusts John D. Co-Reyes overall

1. Sergey Levine
2. Michael Janner
3. Deepak Pathak
4. Abhishek Gupta
5. Benjamin Eysenbach
6. Peter Hase
7. Danijar Hafner
8. Abhinav Kumar Gupta
9. Ayush Jain
10. Pieter Abbeel

Papers from John D. Co-Reyes's perspective

Ordered by how strongly the researchers John D. Co-Reyes 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings (2018) — 159 citations
• EX2: Exploration with Exemplar Models for Deep Reinforcement Learning (2017) — 164 citations
• Entity Abstraction in Visual Model-Based Reinforcement Learning (2019) — 208 citations
• Guiding Policies with Language via Meta-Learning (2018) — 67 citations
• Evolving Reinforcement Learning Algorithms (2021) — 84 citations
• A CCELERATING O NLINE R EINFORCEMENT L EARNING VIA M ODEL -B ASED M ETA -L EARNING (2021)