Roberto Capobianco

Reinforcement Learning · 2016–2022

A researcher in the Science corpus: 3 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 19 citations into it
• Artificial Intelligence — 16 citations into it

Whom Roberto Capobianco trusts overall

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

1. Richard S. Sutton
2. Pieter Abbeel
3. J. Bagnell
4. Marc Peter Deisenroth
5. Stéphane Ross
6. Andrew Y. Ng
7. Andrew Barto
8. Ali Rahimi
9. Weiwei Li
10. Geoffrey E. Hinton

Papers from Roberto Capobianco's perspective

Ordered by how strongly the researchers Roberto Capobianco 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. Gradient-based learning applied to document recognition
4. Reinforcement Learning: An Introduction
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Human-level control through deep reinforcement learning
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Attention is All you Need (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Improved Learning of Dynamics Models for Control (2016) — 41 citations
• Outracing champion Gran Turismo drivers with deep reinforcement learning (2022) — 497 citations
• Reinforcement Learning for Optimization of COVID-19 Mitigation Policies (2020) — 64 citations