Ashley D. Edwards

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 4 papers; trusts 386 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 122 citations into it; #23,060 of 35,345 by trust here, #8,283 by citations
• Reinforcement Learning — 83 citations into it
• Computer Vision and Pattern Recognition — 66 citations into it; #14,294 of 21,575 by trust here, #5,941 by citations
• Control and Systems Engineering — 31 citations into it
• Model Architectures — 16 citations into it
• Optimization & Training — 14 citations into it
• Cognitive Neuroscience — 12 citations into it
• Representation & Self-Supervised Learning — 11 citations into it

Whom Ashley D. Edwards trusts overall

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

1. Diederik P. Kingma
2. Richard S. Sutton
3. Chelsea Finn
4. Geoffrey E. Hinton
5. Pieter Abbeel
6. Sergey Levine
7. Volodymyr Mnih
8. Ilya Sutskever
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Ashley D. Edwards overall

1. Faraz Torabi
2. Nathan Gavenski
3. Garrett Warnell
4. José Antonio Arjona-Medina
5. Peter Stone
6. Ruohan Zhang
7. Sanjeev Arora
8. Jeff Clune
9. Tianhe Yu
10. Avi Singh

Papers from Ashley D. Edwards's perspective

Ordered by how strongly the researchers Ashley D. Edwards 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Generalist Agent (2022) — 1,117 citations
• Imitating Latent Policies from Observation (2018) — 179 citations
• Forward-Backward Reinforcement Learning (2018) — 44 citations
• PERCEPTUAL GOAL SPECIFICATIONS FOR REINFORCEMENT LEARNING (2017)