Carles Gelada

Reinforcement Learning · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 124 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 152 citations into it; #452 of 2,242 by trust here, #876 by citations
• Artificial Intelligence — 142 citations into it; #6,162 of 35,345 by trust here, #5,390 by citations
• Computer Vision and Pattern Recognition — 44 citations into it
• Management Science and Operations Research — 33 citations into it
• Representation & Self-Supervised Learning — 24 citations into it; #2,429 of 3,145 by trust here, #2,066 by citations
• Computational Theory and Mathematics — 19 citations into it
• Statistical Learning Theory — 15 citations into it
• Evaluation & Benchmarks — 13 citations into it

Whom Carles Gelada trusts overall

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

1. Richard S. Sutton
2. Marc G. Bellemare
3. R´emi Munos
4. Doina Precup
5. Will Dabney
6. Geoffrey E. Hinton
7. Andrew Barto
8. Satinder Singh
9. David Silver
10. Yoshua Bengio

Who trusts Carles Gelada overall

1. Shangtong Zhang
2. Ofir Nachum
3. Pablo Samuel Castro
4. Sergey Levine
5. Bo Liu
6. Shimon Whiteson
7. Amy Zhang
8. Aviral Kumar
9. Byung-Jun Lee
10. Mengjiao Yang

Papers from Carles Gelada's perspective

Ordered by how strongly the researchers Carles Gelada 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples (2019) — 701 citations
• DeepMDP: Learning Continuous Latent Space Models for Representation Learning (2019) — 351 citations
• Hyperbolic Discounting and Learning over Multiple Horizons (2019) — 123 citations
• Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift (2019) — 102 citations
• The Importance of Pessimism in Fixed-Dataset Policy Optimization (2020) — 147 citations
• Dopamine: A Research Framework for Deep Reinforcement Learning (2018) — 296 citations