Francesco Faccio

Reinforcement Learning · 2018

A researcher in the Science corpus: 1 paper; trusts 40 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 38 citations into it
• Artificial Intelligence — 26 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Optimization & Training — 8 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Francesco Faccio trusts overall

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

1. Philip S. Thomas
2. Richard S. Sutton
3. Jan Peters
4. Ronald J. Williams
5. Shun-ichi Amari
6. John Schulman
7. Sham M. Kakade
8. Reuven Y. Rubinstein
9. Daan Wierstra
10. Stefan Schaal

Papers from Francesco Faccio's perspective

Ordered by how strongly the researchers Francesco Faccio 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. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
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. Reinforcement Learning: An Introduction
9. Trust Region Policy Optimization (International Conference on Machine Learning)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Policy Optimization via Importance Sampling (2018) — 103 citations