OpenAI

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 4 papers; trusts 176 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 175 citations into it
• Computer Vision and Pattern Recognition — 78 citations into it
• Control and Systems Engineering — 77 citations into it
• Reinforcement Learning — 67 citations into it; #771 of 2,242 by trust here, #721 by citations
• Biomedical Engineering — 41 citations into it
• Robotics & Control — 32 citations into it; #104 of 609 by trust here, #395 by citations
• Representation & Self-Supervised Learning — 31 citations into it
• Optimization & Training — 23 citations into it

Whom OpenAI trusts overall

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

1. Sergey Levine
2. Kaiming He
3. Geoffrey E. Hinton
4. E. Todorov
5. J. Urgen Schmidhuber
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Pieter Abbeel
9. Yoshua Bengio
10. Sepp Hochreiter

Who trusts OpenAI overall

1. Avi Singh
2. Gabriel Dulac-Arnold
3. William H. Guss
4. Gary F. Marcus
5. Priyanka Mandikal
6. Albert Yu
7. Nir Levine
8. Sergey Levine
9. Brandon Houghton
10. Kristen Grauman

Papers from OpenAI's perspective

Ordered by how strongly the researchers OpenAI 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

• Solving Rubik's Cube with a Robot Hand (2019) — 1,497 citations
• GPT-4 Technical Report (2023) — 26,133 citations
• Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision (2023) — 504 citations
• Learning Dexterous In-Hand Manipulation. (2018) — 86 citations