Reese Pathak

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 23 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 12 citations into it; #15,302 of 35,345 by trust here, #26,682 by citations
• Optimization & Training — 12 citations into it
• Computational Mechanics — 7 citations into it
• Federated & Distributed Learning — 6 citations into it

Whom Reese Pathak trusts overall

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

1. Stephen P. Boyd
2. R. Tyrrell Rockafellar
3. Heinz H. Bauschke
4. Steven Diamond
5. H. Brendan McMahan
6. Olivier Devolder
7. Po-Ling Loh
8. Anit Kumar Sahu
9. Yurii E. Nesterov
10. Patrick L. Combettes

Who trusts Reese Pathak overall

1. Yae Jee Cho
2. Khaled Ben Letaief
3. Zachary B. Charles
4. Durmus Alp Emre Acar
5. Jianyu Wang
6. Yuanming Shi
7. Gauri Joshi
8. Yue Zhao
9. Jakub Konecný
10. Jianmin Lu

Papers from Reese Pathak's perspective

Ordered by how strongly the researchers Reese Pathak 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. Gradient-based learning applied to document recognition
4. Learning Multiple Layers of Features from Tiny Images
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
10. Regression Shrinkage and Selection via the Lasso

Their papers

• FedSplit: An algorithmic framework for fast federated optimization (2020) — 214 citations