Carlos Florensa

Reinforcement Learning · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 151 researchers and is trusted by 146 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 126 citations into it; #509 of 2,242 by trust here, #619 by citations
• Artificial Intelligence — 108 citations into it; #1,942 of 35,345 by trust here, #3,978 by citations
• Control and Systems Engineering — 42 citations into it; #1,759 of 8,257 by trust here, #759 by citations
• Computer Vision and Pattern Recognition — 36 citations into it; #11,757 of 21,575 by trust here, #7,657 by citations
• Computational Theory and Mathematics — 24 citations into it; #1,312 of 12,182 by trust here, #1,319 by citations
• Optimization & Training — 16 citations into it
• Robotics & Control — 16 citations into it
• Model Architectures — 13 citations into it

Whom Carlos Florensa trusts overall

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

1. Sergey Levine
2. Pieter Abbeel
3. John Schulman
4. Richard S. Sutton
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. E. Todorov
8. J. Urgen Schmidhuber
9. Diederik P. Kingma
10. Chelsea Finn

Who trusts Carlos Florensa overall

1. Sergey Levine
2. Benjamin Eysenbach
3. Pieter Abbeel
4. Ofir Nachum
5. Corey Lynch
6. Danijar Hafner
7. Ruslan Salakhutdinov
8. Abhishek Gupta
9. Sanmit Narvekar
10. Yoshua Bengio

Papers from Carlos Florensa's perspective

Ordered by how strongly the researchers Carlos Florensa 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Stochastic Neural Networks for Hierarchical Reinforcement Learning (2016) — 378 citations
• Reverse Curriculum Generation for Reinforcement Learning (2017) — 535 citations
• Automatic Goal Generation for Reinforcement Learning Agents (2017) — 582 citations
• Goal-conditioned Imitation Learning (2019) — 277 citations
• Self-supervised Learning of Image Embedding for Continuous Control (2019) — 56 citations