Charlie Beattie

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 7 papers; trusts 6 researchers and is trusted by 72 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 90 citations into it; #34,032 of 35,345 by trust here, #5,720 by citations
• Reinforcement Learning — 77 citations into it; #353 of 2,242 by trust here, #33 by citations
• Computer Vision and Pattern Recognition — 49 citations into it

Whom Charlie Beattie trusts overall

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

1. Volodymyr Mnih
2. Geoffrey E. Hinton
3. Max Jaderberg
4. Alex Graves
5. Richard S. Sutton
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Yoshua Bengio
9. Michal Kempka
10. Yann LeCun

Who trusts Charlie Beattie overall

1. Yuxi Li
2. Michael Crawshaw
3. Thanh Thi Nguyen
4. Arthur Juliani
5. Adam Stooke
6. Joel Z. Leibo
7. Kun Shao
8. Roberta Raileanu
9. Alex Nichol
10. Meire Fortunato

Papers from Charlie Beattie's perspective

Ordered by how strongly the researchers Charlie Beattie trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Going deeper with convolutions
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Massively Parallel Methods for Deep Reinforcement Learning (2015) — 536 citations
• DeepMind Lab (2016) — 267 citations
• Vector-based navigation using grid-like representations in artificial agents (2018) — 716 citations
• A multi-agent reinforcement learning model of common-pool resource appropriation (2017) — 225 citations
• Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents (2018) — 82 citations
• Human-level performance in 3D multiplayer games with population-based reinforcement learning (2018) — 819 citations
• Human-level control through deep reinforcement learning (2015) — 32,431 citations