Joshua Achiam

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 8 papers; trusts 200 researchers and is trusted by 534 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 165 citations into it; #1,233 of 35,345 by trust here, #926 by citations
• Reinforcement Learning — 110 citations into it; #497 of 2,242 by trust here, #648 by citations
• Computer Vision and Pattern Recognition — 41 citations into it; #2,025 of 21,575 by trust here, #2,768 by citations
• Management Science and Operations Research — 24 citations into it; #2,680 of 11,946 by trust here, #2,603 by citations
• Optimization & Training — 22 citations into it; #316 of 2,601 by trust here, #370 by citations
• Representation & Self-Supervised Learning — 19 citations into it
• Computational Theory and Mathematics — 17 citations into it
• Electrical and Electronic Engineering — 14 citations into it

Whom Joshua Achiam trusts overall

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

1. Richard S. Sutton
2. John Schulman
3. Diederik P. Kingma
4. Sergey Levine
5. Geoffrey E. Hinton
6. Pieter Abbeel
7. Volodymyr Mnih
8. Yoshua Bengio
9. Alex Krizhevsky
10. J. Urgen Schmidhuber

Who trusts Joshua Achiam overall

1. Sergey Levine
2. Han-Jia Ye
3. Pieter Abbeel
4. Chelsea Finn
5. Ofir Nachum
6. Yuri Burda
7. Aviral Kumar
8. Danijar Hafner
9. Karthik Narasimhan
10. Greg Yang

Papers from Joshua Achiam's perspective

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

Their papers

• Evaluating Large Language Models Trained on Code (2021) — 10,663 citations
• On First-Order Meta-Learning Algorithms (2018) — 2,619 citations
• Constrained Policy Optimization (2017) — 1,938 citations
• Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning (2017) — 251 citations
• Variational Option Discovery Algorithms (2018) — 209 citations
• Towards Characterizing Divergence in Deep Q-Learning (2019) — 105 citations
• Responsive Safety in Reinforcement Learning by PID Lagrangian Methods (2020) — 463 citations
• GPT-4o System Card (2024) — 4,622 citations