Christopher Hesse

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 9 papers; trusts 47 researchers and is trusted by 181 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 287 citations into it; #1,795 of 35,345 by trust here, #90 by citations
• Computer Vision and Pattern Recognition — 105 citations into it
• Reinforcement Learning — 73 citations into it; #690 of 2,242 by trust here, #379 by citations
• Evaluation & Benchmarks — 63 citations into it; #900 of 3,147 by trust here, #155 by citations
• Model Architectures — 33 citations into it
• Optimization & Training — 29 citations into it
• Management Science and Operations Research — 17 citations into it

Whom Christopher Hesse trusts overall

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

1. Diederik P. Kingma
2. John Schulman
3. Sepp Hochreiter
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Volodymyr Mnih
7. J. Urgen Schmidhuber
8. Jimmy Ba
9. Alex Krizhevsky
10. Richard S. Sutton

Who trusts Christopher Hesse overall

1. Roberta Raileanu
2. Arthur Juliani
3. Danijar Hafner
4. Minqi Jiang
5. Kimin Lee
6. Rishabh Agarwal
7. Karl Cobbe
8. Maxwell Goldstein
9. Michael Laskin
10. Adam Stooke

Papers from Christopher Hesse's perspective

Ordered by how strongly the researchers Christopher Hesse 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. Gradient-based learning applied to document recognition
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Language Models are Few-Shot Learners (2020) — 61,005 citations
• Evaluating Large Language Models Trained on Code (2021) — 10,663 citations
• Training Verifiers to Solve Math Word Problems (2021) — 9,717 citations
• WebGPT: Browser-assisted question-answering with human feedback (2021) — 1,953 citations
• Quantifying Generalization in Reinforcement Learning (2018) — 782 citations
• Dota 2 with Large Scale Deep Reinforcement Learning (2019) — 2,179 citations
• Scaling Laws for Autoregressive Generative Modeling (2020) — 639 citations
• Gotta Learn Fast: A New Benchmark for Generalization in RL (2018) — 189 citations
• Leveraging Procedural Generation to Benchmark Reinforcement Learning (2019) — 721 citations