Edward Hughes

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 12 papers; trusts 88 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 324 citations into it; #19,829 of 35,345 by trust here, #6,229 by citations
• Reinforcement Learning — 246 citations into it; #1,337 of 2,242 by trust here, #485 by citations
• Computer Vision and Pattern Recognition — 83 citations into it
• Management Science and Operations Research — 77 citations into it
• Game & Decision Theory — 75 citations into it
• Sociology and Political Science — 61 citations into it
• Natural Language Processing — 48 citations into it
• Model Architectures — 17 citations into it

Whom Edward Hughes trusts overall

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

1. Richard S. Sutton
2. Michael P. Wellman
3. Volodymyr Mnih
4. G. Tesauro
5. Geoffrey E. Hinton
6. J. Urgen Schmidhuber
7. Andrew Barto
8. Ronald J. Williams
9. Yoshua Bengio
10. Satinder Singh

Who trusts Edward Hughes overall

1. Sven Gronauer
2. Hengyuan Hu
3. Tonghan Wang
4. Georgios Papoudakis
5. Jiechuan Jiang
6. Klaus-Jürgen Diepold
7. James Goodman
8. Jakob N. Foerster
9. Andrea Tacchetti
10. Joel Z. Leibo

Papers from Edward Hughes's perspective

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

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

• Learning to Understand Goal Specifications by Modelling Reward (2018) — 167 citations
• Causal Reasoning from Meta-reinforcement Learning (2019) — 138 citations
• Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research (2019) — 125 citations
• Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning (2018) — 170 citations
• The Hanabi Challenge: A New Frontier for AI Research (2019) — 429 citations
• Malthusian Reinforcement Learning (2018) — 43 citations
• Inequity aversion improves cooperation in intertemporal social dilemmas (2018) — 271 citations
• Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning (2020) — 103 citations
• Human-Timescale Adaptation in an Open-Ended Task Space (2023) — 163 citations
• OpenSpiel: A Framework for Reinforcement Learning in Games (2019) — 323 citations
• Learning to Follow Language Instructions with Adversarial Reward Induction (2018) — 33 citations
• Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning (2018) — 553 citations