Rowan Thomas McAllister

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 8 papers; trusts 204 researchers and is trusted by 150 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #2,640 of 35,345 by trust here, #4,627 by citations
• Reinforcement Learning — 81 citations into it; #203 of 2,242 by trust here, #445 by citations
• Computer Vision and Pattern Recognition — 56 citations into it
• Control and Systems Engineering — 42 citations into it
• Automotive Engineering — 26 citations into it; #1,013 of 3,081 by trust here, #1,176 by citations
• Probabilistic & Bayesian Methods — 23 citations into it; #200 of 1,525 by trust here, #224 by citations
• Computational Theory and Mathematics — 20 citations into it; #6,046 of 12,182 by trust here, #10,901 by citations
• Robotics & Control — 15 citations into it; #513 of 609 by trust here, #489 by citations

Whom Rowan Thomas McAllister trusts overall

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

1. Marc Peter Deisenroth
2. Diederik P. Kingma
3. Carl E. Rasmussen
4. Sergey Levine
5. Geoffrey E. Hinton
6. Yarin Gal
7. Richard S. Sutton
8. Alex Krizhevsky
9. Yoshua Bengio
10. Pieter Abbeel

Who trusts Rowan Thomas McAllister overall

1. Sergey Levine
2. Yarin Gal
3. David R Ha
4. Michael Janner
5. Adrian Weller
6. Danijar Hafner
7. J. Urgen Schmidhuber
8. Pieter Abbeel
9. Scott Fujimoto
10. Stefan Depeweg

Papers from Rowan Thomas McAllister's perspective

Ordered by how strongly the researchers Rowan Thomas McAllister 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models (2018) — 1,543 citations
• Learning Invariant Representations for Reinforcement Learning without Reconstruction (2020) — 593 citations
• PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings (2019) — 414 citations
• Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs (2017) — 37 citations
• Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning (2017) — 345 citations
• Data-Efficient Reinforcement Learning in Continuous-State POMDPs (2016) — 12 citations
• Deep Imitative Models for Flexible Inference, Planning, and Control (2018) — 160 citations
• Improving PILCO with Bayesian Neural Network Dynamics Models (2016)