Ryan Webster

Generative Models · 2019

A researcher in the Science corpus: 2 papers; trusts 212 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Generative Models — 47 citations into it; #1,429 of 1,956 by trust here, #1,681 by citations
• Computer Vision and Pattern Recognition — 45 citations into it; #15,069 of 21,575 by trust here, #16,787 by citations
• Artificial Intelligence — 21 citations into it; #24,358 of 35,345 by trust here, #25,809 by citations
• Evaluation & Benchmarks — 8 citations into it; #2,182 of 3,147 by trust here, #2,743 by citations

Whom Ryan Webster trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Kaiming He
6. Yann LeCun
7. Christian Szegedy
8. Karen Simonyan
9. Alec Radford
10. Ilya Sutskever

Who trusts Ryan Webster overall

1. Rameen Abdal
2. Gerrit J. J. van den Burg
3. Michael Albright
4. Katherine Lee
5. Christopher K. I. Williams
6. Scott McCloskey
7. Yipeng Qin
8. Peter Wonka

Papers from Ryan Webster's perspective

Ordered by how strongly the researchers Ryan Webster trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. GENERATIVE ADVERSARIAL NETS
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Detecting Overfitting of Deep Generative Networks via Latent Recovery (2019) — 107 citations
• Revisiting Precision and Recall Definition for Generative Model Evaluation (2019) — 20 citations