Jesse Bettencourt

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 45 researchers and is trusted by 274 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it; #1,997 of 35,345 by trust here, #2,774 by citations
• Computer Vision and Pattern Recognition — 27 citations into it; #2,399 of 21,575 by trust here, #2,102 by citations
• Numerical Methods & Scientific Computing — 27 citations into it
• Statistical and Nonlinear Physics — 27 citations into it; #185 of 10,127 by trust here, #181 by citations
• Model Architectures — 24 citations into it; #328 of 2,278 by trust here, #416 by citations
• Probabilistic & Bayesian Methods — 22 citations into it
• Generative Models — 19 citations into it; #696 of 1,956 by trust here, #616 by citations
• Optimization & Training — 14 citations into it

Whom Jesse Bettencourt trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Lawrence F. Shampine
4. Danilo Jimenez Rezende
5. Yoshua Bengio
6. Kaiming He
7. Ernst Hairer
8. Max Welling
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Jesse Bettencourt overall

1. Yang Song
2. Ricky T. Q. Chen
3. Arash Vahdat
4. David Kristjanson Duvenaud
5. Jonas Köhler
6. Will Grathwohl
7. Chin-Wei Huang
8. Shitong Luo
9. Conor Durkan
10. Shian Du

Papers from Jesse Bettencourt's perspective

Ordered by how strongly the researchers Jesse Bettencourt 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. GENERATIVE ADVERSARIAL NETS
9. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Neural Ordinary Differential Equations (2018) — 7,349 citations
• FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models (2018) — 1,080 citations
• Learning Differential Equations that are Easy to Solve (2020) — 131 citations
• Joint Importance Sampling for Variational Inference (2018) — 5 citations