Matteo Papini

Reinforcement Learning · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 67 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 65 citations into it
• Artificial Intelligence — 59 citations into it
• Optimization & Training — 26 citations into it
• Computational Theory and Mathematics — 16 citations into it
• Management Science and Operations Research — 15 citations into it
• Control and Systems Engineering — 10 citations into it
• Computational Mechanics — 9 citations into it
• Statistics and Probability — 6 citations into it

Whom Matteo Papini trusts overall

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

1. Richard S. Sutton
2. Jan Peters
3. Ronald J. Williams
4. Philip S. Thomas
5. Matteo Pirotta
6. Stefan Schaal
7. Jonathan Baxter
8. Sham M. Kakade
9. Geoffrey E. Hinton
10. Andrew Barto

Who trusts Matteo Papini overall

1. Amal Feriani
2. Ching-An Cheng
3. Ekram Hossain
4. Romain Laroche
5. P. Liotet
6. Xinyan Yan

Papers from Matteo Papini's perspective

Ordered by how strongly the researchers Matteo Papini 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Policy Optimization via Importance Sampling (2018) — 103 citations
• Stochastic Variance-Reduced Policy Gradient (2018) — 213 citations
• Adaptive Batch Size for Safe Policy Gradients (2017) — 37 citations