Adam Gleave

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 4 papers; trusts 129 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 86 citations into it
• Reinforcement Learning — 46 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Natural Language Processing — 15 citations into it
• Control and Systems Engineering — 10 citations into it
• Large Language Models — 7 citations into it
• Optimization & Training — 7 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Adam Gleave trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Jimmy Ba
5. Alex Krizhevsky
6. Ilya Sutskever
7. Pieter Abbeel
8. Yann LeCun
9. Kaiming He
10. Richard S. Sutton

Who trusts Adam Gleave overall

1. Amal Feriani
2. Antonin Raffin
3. Sam Toyer
4. Ekram Hossain

Papers from Adam Gleave's perspective

Ordered by how strongly the researchers Adam Gleave 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Multi-task Maximum Entropy Inverse Reinforcement Learning (2018) — 42 citations
• Adversarial Policies: Attacking Deep Reinforcement Learning (2019) — 437 citations
• Uncertainty Estimation for Language Reward Models (2022) — 41 citations
• Stable-Baselines3: Reliable Reinforcement Learning Implementations (2021) — 2,998 citations