Pablo Samuel Castro

Artificial Intelligence · 2010–2023

A researcher in the Science corpus: 14 papers; trusts 373 researchers and is trusted by 52 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 297 citations into it; #4,656 of 35,345 by trust here, #5,970 by citations
• Reinforcement Learning — 243 citations into it; #557 of 2,242 by trust here, #690 by citations
• Computer Vision and Pattern Recognition — 92 citations into it
• Management Science and Operations Research — 41 citations into it
• Representation & Self-Supervised Learning — 35 citations into it
• Computational Theory and Mathematics — 33 citations into it; #1,647 of 12,182 by trust here, #2,689 by citations
• Evaluation & Benchmarks — 31 citations into it
• Natural Language Processing — 27 citations into it

Whom Pablo Samuel Castro trusts overall

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

1. Marc G. Bellemare
2. Richard S. Sutton
3. Doina Precup
4. Diederik P. Kingma
5. Andrew Barto
6. Geoffrey E. Hinton
7. Volodymyr Mnih
8. Will Dabney
9. Alex Krizhevsky
10. Yoshua Bengio

Who trusts Pablo Samuel Castro overall

1. Rishabh Agarwal
2. Ofir Nachum
3. Marc G. Bellemare
4. Mengjiao Yang
5. Carles Gelada
6. Sergey Levine
7. William Fedus
8. Danijar Hafner
9. Robert Dadashi
10. Philippe Hansen-Estruch

Papers from Pablo Samuel Castro's perspective

Ordered by how strongly the researchers Pablo Samuel Castro 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Rigging the Lottery: Making All Tickets Winners (2019) — 758 citations
• Deep Reinforcement Learning at the Edge of the Statistical Precipice (2021) — 950 citations
• Autonomous navigation of stratospheric balloons using reinforcement learning (2020) — 408 citations
• An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents (2018) — 55 citations
• Scalable methods for computing state similarity in deterministic Markov Decision Processes (2019) — 184 citations
• Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning (2021) — 197 citations
• A Geometric Perspective on Optimal Representations for Reinforcement Learning (2019) — 99 citations
• A Comparative Analysis of Expected and Distributional Reinforcement Learning (2019) — 97 citations
• Distributional reinforcement learning with linear function approximation (2019) — 28 citations
• Using Bisimulation for Policy Transfer in MDPs (2010) — 70 citations
• Automatic Construction of Temporally Extended Actions for MDPs Using Bisimulation Metrics (2011) — 29 citations
• Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks (2023) — 410 citations
• Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue (2019) — 14 citations
• Dopamine: A Research Framework for Deep Reinforcement Learning (2018) — 296 citations