Peter Sorrenson

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 2 papers; trusts 50 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 25 citations into it; #14,280 of 21,575 by trust here, #17,757 by citations
• Artificial Intelligence — 24 citations into it; #23,015 of 35,345 by trust here, #27,551 by citations
• Generative Models — 21 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it
• Representation & Self-Supervised Learning — 13 citations into it; #2,258 of 3,145 by trust here, #2,757 by citations
• Signal Processing — 12 citations into it; #4,799 of 7,572 by trust here, #5,570 by citations

Whom Peter Sorrenson trusts overall

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

1. Diederik P. Kingma
2. Laurent Dinh
3. Aapo Hyvärinen
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Gregory Cohen
7. Matthew J. Johnson
8. Alex Krizhevsky
9. Jimmy Ba
10. Yann LeCun

Who trusts Peter Sorrenson overall

1. Francesco Locatello
2. Hermanni Hälvä
3. Shifeng Zhang
4. Andrea Dittadi
5. Aapo Hyvärinen
6. Geoffrey Roeder
7. Chen Zhang
8. Bernhard Schölkopf
9. Ben Poole
10. Sébastien Lachapelle

Papers from Peter Sorrenson's perspective

Ordered by how strongly the researchers Peter Sorrenson 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. GENERATIVE ADVERSARIAL NETS
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN) (2020) — 137 citations
• Disentanglement through Nonlinear ICA with General Incompressible-flow Networks (GIN) (2020)