Derek Onken

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 1 paper; trusts 43 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 18 citations into it; #13,154 of 21,575 by trust here, #16,787 by citations
• Artificial Intelligence — 17 citations into it; #21,186 of 35,345 by trust here, #25,809 by citations
• Probabilistic & Bayesian Methods — 16 citations into it
• Generative Models — 14 citations into it
• Statistical and Nonlinear Physics — 12 citations into it; #5,515 of 10,127 by trust here, #6,421 by citations
• Applied Mathematics — 5 citations into it
• Optimization & Training — 5 citations into it
• Statistics and Probability — 5 citations into it

Whom Derek Onken trusts overall

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

1. Diederik P. Kingma
2. Danilo Jimenez Rezende
3. George Papamakarios
4. Tim Salimans
5. Michael F. Hutchinson
6. Radford M. Neal
7. Geoffrey E. Hinton
8. Ernst Hairer
9. Akinori Tanaka
10. Max Welling

Who trusts Derek Onken overall

1. Lars Ruthotto
2. Michael S. Albergo
3. Shian Du
4. Eldad Haber
5. Nicholas Matthew Boffi
6. Eric Vanden-Eijnden
7. Yihong Luo

Papers from Derek Onken's perspective

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

Their papers

• OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport (2020) — 217 citations