Marcos Ricardo Omena de Albuquerque Máximo

Reinforcement Learning · 2022

A researcher in the Science corpus: 1 paper; trusts 91 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 57 citations into it
• Artificial Intelligence — 56 citations into it
• Management Science and Operations Research — 17 citations into it
• Computational Theory and Mathematics — 14 citations into it
• Electrical and Electronic Engineering — 10 citations into it
• Control and Systems Engineering — 9 citations into it
• Evaluation & Benchmarks — 9 citations into it

Whom Marcos Ricardo Omena de Albuquerque Máximo trusts overall

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

1. Richard S. Sutton
2. Sergey Levine
3. Aviral Kumar
4. Geoffrey E. Hinton
5. Scott Fujimoto
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Yoshua Bengio
9. Justin Fu
10. Doina Precup

Papers from Marcos Ricardo Omena de Albuquerque Máximo's perspective

Ordered by how strongly the researchers Marcos Ricardo Omena de Albuquerque Máximo 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. Reinforcement Learning: An Introduction
9. Human-level control through deep reinforcement learning
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems (2022) — 424 citations