Peter Toth

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 24 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #22,518 of 35,345 by trust here, #21,878 by citations
• Computer Vision and Pattern Recognition — 31 citations into it
• Generative Models — 25 citations into it; #1,306 of 1,956 by trust here, #1,635 by citations
• Probabilistic & Bayesian Methods — 24 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Management Science and Operations Research — 6 citations into it
• Optimization & Training — 6 citations into it
• Mathematical Physics — 5 citations into it

Whom Peter Toth trusts overall

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

1. Diederik P. Kingma
2. Radford M. Neal
3. Danilo Jimenez Rezende
4. Geoffrey E. Hinton
5. Max Welling
6. Yoshua Bengio
7. Tim Salimans
8. Jimmy Ba
9. Alex Krizhevsky
10. Laurent Dinh

Who trusts Peter Toth overall

1. Jonas Köhler
2. Hao Wu
3. Miles Cranmer
4. Frank No'e
5. Jared D. Willard

Papers from Peter Toth's perspective

Ordered by how strongly the researchers Peter Toth 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. 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. ImageNet: A large-scale hierarchical image database
9. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Online Learning with Gated Linear Networks (2017) — 26 citations
• Equivariant Hamiltonian Flows (2019) — 72 citations
• Hamiltonian Generative Networks (2019) — 255 citations