Yu-Guan Hsieh

Optimization & Training · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 24 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 30 citations into it; #2,400 of 2,601 by trust here, #2,215 by citations
• Artificial Intelligence — 27 citations into it; #31,037 of 35,345 by trust here, #27,551 by citations
• Computational Mechanics — 20 citations into it; #8,755 of 10,001 by trust here, #7,173 by citations
• Computational Theory and Mathematics — 18 citations into it; #10,687 of 12,182 by trust here, #8,756 by citations
• Numerical Analysis — 15 citations into it
• Management Science and Operations Research — 12 citations into it
• Generative Models — 11 citations into it
• Mathematical Optimization — 10 citations into it

Whom Yu-Guan Hsieh trusts overall

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

1. Gauthier Gidel
2. Yura Malitsky
3. Yurii E. Nesterov
4. Arkadi Nemirovski
5. Paul Tseng
6. Alexander Rakhlin
7. Tengyuan Liang
8. Francisco Facchinei
9. Constantinos Daskalakis
10. Tatjana Chavdarova

Who trusts Yu-Guan Hsieh overall

1. Alina Ene
2. Noah Golowich
3. Chaobing Song
4. Huy L. Nguyen
5. Sarath Pattathil
6. Nicolas Loizou
7. Zhengyuan Zhou

Papers from Yu-Guan Hsieh's perspective

Ordered by how strongly the researchers Yu-Guan Hsieh 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. GENERATIVE ADVERSARIAL NETS
5. Learning Multiple Layers of Features from Tiny Images
6. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Long Short-Term Memory

Their papers

• On the convergence of single-call stochastic extra-gradient methods (2019) — 189 citations
• Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling (2020) — 80 citations