Matteo Turchetta

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 26 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #18,870 of 35,345 by trust here, #9,968 by citations
• Reinforcement Learning — 29 citations into it; #1,366 of 2,242 by trust here, #916 by citations
• Control and Systems Engineering — 23 citations into it; #3,916 of 8,257 by trust here, #1,099 by citations
• Computer Vision and Pattern Recognition — 19 citations into it; #14,863 of 21,575 by trust here, #12,689 by citations
• Computational Theory and Mathematics — 13 citations into it
• Robotics & Control — 7 citations into it
• Management Science and Operations Research — 6 citations into it
• Optimization & Training — 5 citations into it

Whom Matteo Turchetta trusts overall

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

1. Christopher K. I. Williams
2. Richard S. Sutton
3. Subhashis Ghosal
4. Andrew Barto
5. Alexander Hans
6. Jan Peters
7. Felix Berkenkamp
8. Niranjan Srinivas
9. Andreas Krause
10. Pieter Abbeel

Who trusts Matteo Turchetta overall

1. Gabriel Dulac-Arnold
2. Shangding Gu
3. Lukas Brunke
4. Daniel Jaymin Mankowitz
5. Dario Amodei
6. Richard Cheng
7. Victoria Krakovna
8. Yinlam Chow
9. Felix Berkenkamp
10. Melissa Greeff

Papers from Matteo Turchetta's perspective

Ordered by how strongly the researchers Matteo Turchetta 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. Reinforcement Learning: An Introduction
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. Human-level control through deep reinforcement learning

Their papers

• Safe Model-based Reinforcement Learning with Stability Guarantees (2017) — 1,021 citations
• Safe Exploration in Finite Markov Decision Processes with Gaussian Processes (2016) — 204 citations
• Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations (2018) — 184 citations
• Learning-Based Model Predictive Control for Safe Exploration (2018) — 439 citations