André Barreto

Reinforcement Learning · 2016–2021

A researcher in the Science corpus: 8 papers; trusts 86 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 177 citations into it; #773 of 2,242 by trust here, #458 by citations
• Artificial Intelligence — 146 citations into it; #15,127 of 35,345 by trust here, #4,829 by citations
• Computer Vision and Pattern Recognition — 32 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Management Science and Operations Research — 24 citations into it
• Control and Systems Engineering — 23 citations into it
• Computational Theory and Mathematics — 22 citations into it; #10,843 of 12,182 by trust here, #1,157 by citations
• Optimization & Training — 11 citations into it

Whom André Barreto trusts overall

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

1. Richard S. Sutton
2. Csaba Szepesvari
3. Martin L. Puterman
4. Andrew Barto
5. Dimitri P. Bertsekas
6. Peter S. Dayan
7. Geoffrey E. Hinton
8. Ronald J. Williams
9. David Silver
10. Diederik P. Kingma

Who trusts André Barreto overall

1. Michael Janner
2. Sergey Levine
3. Amy Zhang
4. Ida Momennejad
5. David Pfau
6. Daniel S. Brown
7. Amir-massoud Farahmand
8. Karthik Narasimhan
9. Romain Laroche
10. Marlos C. Machado

Papers from André Barreto's perspective

Ordered by how strongly the researchers André Barreto 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Reinforcement Learning: An Introduction

Their papers

• The Predictron: End-To-End Learning and Planning (2016) — 310 citations
• Successor Features for Transfer in Reinforcement Learning (2016) — 725 citations
• Unicorn: Continual Learning with a Universal, Off-policy Agent (2018) — 48 citations
• Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement (2018) — 192 citations
• Fast deep reinforcement learning using online adjustments from the past (2018) — 47 citations
• The Value Equivalence Principle for Model-Based Reinforcement Learning (2020) — 103 citations
• The Option Keyboard: Combining Skills in Reinforcement Learning (2021) — 116 citations
• Value-Aware Loss Function for Model-based Reinforcement Learning (2017) — 133 citations