Peyman Mohajerin Esfahani
Management Science and Operations Research · 2015–2019
A researcher in the Science corpus: 4 papers; trusts 97 researchers and is trusted by 32 researchers here, as inferred from the citation graph.
Subfields
- Management Science and Operations Research — 52 citations into it; #972 of 11,946 by trust here, #1,559 by citations
- Statistics and Probability — 48 citations into it; #4,833 of 12,932 by trust here, #4,517 by citations
- Artificial Intelligence — 30 citations into it; #28,106 of 35,345 by trust here, #25,809 by citations
- Computer Vision and Pattern Recognition — 26 citations into it
- Optimization & Training — 22 citations into it
- Applied Mathematics — 18 citations into it
- Computational Theory and Mathematics — 18 citations into it
- Control and Systems Engineering — 18 citations into it
Whom Peyman Mohajerin Esfahani trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Marco Cuturi
- Elisabeth Köbis
- Cédric Villani
- Johan Efberg
- Laurent El Ghaoui
- Yann LeCun
- Emre Erdogan
- Zhaolin Hu
- Nicolas G. Fournier
- Dimitri P. Bertsekas
Who trusts Peyman Mohajerin Esfahani overall
- John C. Duchi
- Soroosh Shafieezadeh-Abadeh
- Jose H. Blanchet
- Hongseok Namkoong
- Hamed Rahimian
- Ruidi Chen
- D. Kühn
- Zhi Chen
- Henry Lam
- Marvin Zhang
Papers from Peyman Mohajerin Esfahani's perspective
Ordered by how strongly the researchers Peyman Mohajerin Esfahani trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- GENERATIVE ADVERSARIAL NETS
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations (2015) — 2,107 citations
- Distributionally Robust Logistic Regression (2015) — 353 citations
- Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning (2019) — 511 citations
- Regularization via Mass Transportation (2017) — 245 citations