Ernest K. Ryu

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 3 papers; trusts 56 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 51 citations into it
• Artificial Intelligence — 38 citations into it
• Optimization & Training — 33 citations into it; #1,370 of 2,601 by trust here, #2,162 by citations
• Computational Mechanics — 31 citations into it
• Computational Theory and Mathematics — 28 citations into it
• Generative Models — 24 citations into it
• Numerical Analysis — 20 citations into it
• Mathematical Optimization — 15 citations into it

Whom Ernest K. Ryu trusts overall

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

1. Patrick L. Combettes
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Antonin Chambolle
5. R. Tyrrell Rockafellar
6. Heinz H. Bauschke
7. Geoffrey E. Hinton
8. Kaiming He
9. Thomas Pock
10. M. Powell

Who trusts Ernest K. Ryu overall

1. Brendan O'Donoghue
2. Balamurugan Palaniappan
3. Junfeng Wen
4. Yu-Guan Hsieh
5. Nicolas Loizou
6. Francis R. Bach
7. Chong Mou
8. Bo Dai
9. Franck Iutzeler
10. Reese Pathak

Papers from Ernest K. Ryu's perspective

Ordered by how strongly the researchers Ernest K. Ryu 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. Long Short-Term Memory
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Plug-and-Play Methods Provably Converge with Properly Trained Denoisers (2019) — 451 citations
• ODE Analysis of Stochastic Gradient Methods with Optimism and Anchoring for Minimax Problems and GANs (2019) — 43 citations
• A Primer on Monotone Operator Methods (2015) — 386 citations