Luis Montesano

Control and Systems Engineering · 2008–2012

A researcher in the Science corpus: 7 papers; trusts 48 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 37 citations into it; #1,036 of 8,257 by trust here, #1,064 by citations
• Artificial Intelligence — 29 citations into it; #5,741 of 35,345 by trust here, #10,065 by citations
• Social Psychology — 23 citations into it
• Reinforcement Learning — 20 citations into it
• Biomedical Engineering — 13 citations into it; #4,861 of 10,734 by trust here, #8,749 by citations
• Computer Vision and Pattern Recognition — 12 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Robotics & Control — 8 citations into it

Whom Luis Montesano trusts overall

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

1. Manuel Lopes
2. David E. Heckerman
3. Jonas Mockus
4. Trevor J. Hastie
5. Bernhard Schölkopf
6. Andrew Y. Ng
7. Ashutosh Saxena
8. José Santos-Victor
9. Stefan Schaal
10. Simon Tong

Who trusts Luis Montesano overall

1. Manuel Lopes
2. Oliver Kroemer
3. Jeffrey Mahler
4. Lerrel Pinto
5. Scott Niekum
6. Sergey Levine
7. Abhinav Kumar Gupta
8. Peter Pastor
9. Pierre-Yves Oudeyer
10. Aitor Aldoma

Papers from Luis Montesano's perspective

Ordered by how strongly the researchers Luis Montesano 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Reinforcement Learning: An Introduction
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Active Learning for Reward Estimation in Inverse Reinforcement Learning (2009) — 217 citations
• Learning Object Affordances: From Sensory--Motor Coordination to Imitation (2008) — 438 citations
• Learning grasping affordances from local visual descriptors (2009) — 105 citations
• Active learning of visual descriptors for grasping using non-parametric smoothed beta distributions (2012) — 47 citations
• Body schema acquisition through active learning (2010) — 52 citations
• Abstraction Levels for Robotic Imitation: Overview and Computational Approaches (2010) — 50 citations
• The iCub humanoid robot: An open-systems platform for research in cognitive development (2010) — 623 citations