Tom Zahavy

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 8 papers; trusts 318 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 159 citations into it; #6,905 of 35,345 by trust here, #4,979 by citations
• Reinforcement Learning — 123 citations into it; #692 of 2,242 by trust here, #510 by citations
• Computer Vision and Pattern Recognition — 62 citations into it
• Optimization & Training — 29 citations into it
• Management Science and Operations Research — 22 citations into it; #8,809 of 11,946 by trust here, #4,430 by citations
• Representation & Self-Supervised Learning — 22 citations into it
• Computational Theory and Mathematics — 19 citations into it
• Natural Language Processing — 17 citations into it

Whom Tom Zahavy trusts overall

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

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

Who trusts Tom Zahavy overall

1. Prithviraj Ammanabrolu
2. Nir Ben-Zrihem
3. Matthew J. Hausknecht
4. Tom Everitt
5. Santhosh K. Ramakrishnan
6. Raghuram Mandyam Annasamy
7. Jinkyu Kim
8. Sam Greydanus
9. Christian Rupprecht
10. Sebastian Lapuschkin

Papers from Tom Zahavy's perspective

Ordered by how strongly the researchers Tom Zahavy 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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Deep Hierarchical Approach to Lifelong Learning in Minecraft (2016) — 404 citations
• Graying the black box: Understanding DQNs (2016) — 292 citations
• Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning (2018) — 218 citations
• Self-Tuning Deep Reinforcement Learning (2020) — 15 citations
• Bootstrapped Meta-Learning (2021) — 62 citations
• Visualizing Dynamics: from t-SNE to SEMI-MDPs (2016) — 13 citations
• Is a picture worth a thousand words? A Deep Multi-Modal Fusion Architecture for Product Classification in e-commerce (2016) — 68 citations
• Spatio-Temporal Abstractions in Reinforcement Learning Through Neural Encoding (2016) — 4 citations