Teodor Mihai Moldovan

Artificial Intelligence · 2012–2015

A researcher in the Science corpus: 4 papers; trusts 39 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it; #5,950 of 35,345 by trust here, #10,065 by citations
• Reinforcement Learning — 31 citations into it; #540 of 2,242 by trust here, #926 by citations
• Control and Systems Engineering — 16 citations into it; #7,322 of 8,257 by trust here, #3,004 by citations
• Computational Theory and Mathematics — 8 citations into it; #1,497 of 12,182 by trust here, #2,419 by citations
• Computer Networks and Communications — 8 citations into it
• Management Science and Operations Research — 8 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Teodor Mihai Moldovan trusts overall

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

1. Richard S. Sutton
2. Marc Peter Deisenroth
3. Andrew Barto
4. Michael I. Jordan
5. Shie Mannor
6. John N. Tsitsiklis
7. Andrew Y. Ng
8. Arnab Nilim
9. Yee Whye Teh
10. Dimitri P. Bertsekas

Who trusts Teodor Mihai Moldovan overall

1. Javier García
2. Jason Pazis
3. Victoria Krakovna
4. Felix Berkenkamp
5. Fernando Fernández
6. Sylvain Calinon
7. Shangding Gu
8. Ronald E. Parr
9. Chao Yu
10. Laurent Orseau

Papers from Teodor Mihai Moldovan's perspective

Ordered by how strongly the researchers Teodor Mihai Moldovan 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Reinforcement Learning: An Introduction
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Safe Exploration in Markov Decision Processes (2012) — 333 citations
• Model-based reinforcement learning with parametrized physical models and optimism-driven exploration (2015) — 53 citations
• Optimism-driven exploration for nonlinear systems (2015) — 37 citations
• Risk Aversion in Markov Decision Processes via Near Optimal Chernoff Bounds (2012) — 50 citations