Fernando Fernández

Artificial Intelligence · with García · 2006–2015

A researcher in the Science corpus: 4 papers; trusts 58 researchers and is trusted by 83 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it; #2,889 of 35,345 by trust here, #5,600 by citations
• Reinforcement Learning — 36 citations into it; #579 of 2,242 by trust here, #734 by citations
• Control and Systems Engineering — 12 citations into it; #821 of 8,257 by trust here, #618 by citations
• Computational Theory and Mathematics — 10 citations into it; #2,812 of 12,182 by trust here, #5,239 by citations
• Management Science and Operations Research — 5 citations into it

Whom Fernando Fernández trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Ben J. A. Kröse
4. Rogier Koppejan
5. Pieter Abbeel
6. Peter Stone
7. William D. Smart
8. Leslie Pack Kaelbling
9. Matthias Heger
10. Matthew E. Taylor

Who trusts Fernando Fernández overall

1. Matthew E. Taylor
2. Javier García
3. Peter Stone
4. W. B. Knox
5. Stefan Depeweg
6. Timothy A. Mann
7. Andrew M. Saxe
8. Edouard Leurent
9. Shivaram Kalyanakrishnan
10. Jinhua Song

Papers from Fernando Fernández's perspective

Ordered by how strongly the researchers Fernando Fernández 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. Reinforcement Learning: An Introduction
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A comprehensive survey on safe reinforcement learning (2015) — 2,055 citations
• Probabilistic policy reuse in a reinforcement learning agent (2006) — 356 citations
• Safe Exploration of State and Action Spaces in Reinforcement Learning (2012) — 173 citations
• Reinforcement learning in the robocup-soccer keepaway (2007) — 396 citations