Mark Cutler

Artificial Intelligence · 2014–2015

A researcher in the Science corpus: 2 papers; trusts 34 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it; #7,889 of 35,345 by trust here, #15,542 by citations
• Reinforcement Learning — 15 citations into it; #712 of 2,242 by trust here, #1,371 by citations
• Computational Theory and Mathematics — 7 citations into it
• Control and Systems Engineering — 7 citations into it; #2,019 of 8,257 by trust here, #4,262 by citations

Whom Mark Cutler trusts overall

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

1. Richard S. Sutton
2. Pieter Abbeel
3. Marc Peter Deisenroth
4. Martin L. Puterman
5. Andrew Y. Ng
6. G. Tesauro
7. Michael L. Littman
8. J. Bagnell
9. J. Kolter
10. Andrew Barto

Who trusts Mark Cutler overall

1. Fereshteh Sadeghi
2. Kirthevasan Kandasamy
3. Sergey Levine
4. Sanket Kamthe
5. Gautam Dasarathy
6. Shaojun Zhu
7. Marc Peter Deisenroth
8. Sergey I. Nikolenko
9. Yilun Du
10. Josiah P. Hanna

Papers from Mark Cutler's perspective

Ordered by how strongly the researchers Mark Cutler 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. Reinforcement Learning: An Introduction
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Efficient reinforcement learning for robots using informative simulated priors (2015) — 87 citations
• Reinforcement learning with multi-fidelity simulators (2014) — 93 citations