Max Simchowitz
Computational Mechanics · with Recht · 2015–2018
A researcher in the Science corpus: 4 papers; trusts 20 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Computational Mechanics — 42 citations into it; #8,382 of 10,001 by trust here, #3,763 by citations
- Optimization & Training — 24 citations into it
- Artificial Intelligence — 19 citations into it; #20,264 of 35,345 by trust here, #12,761 by citations
- Numerical Analysis — 16 citations into it
- Signal Processing — 15 citations into it
- Computer Vision and Pattern Recognition — 13 citations into it
- Statistics and Probability — 12 citations into it; #7,478 of 12,932 by trust here, #3,411 by citations
- Computational Theory and Mathematics — 9 citations into it
Whom Max Simchowitz trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yurii E. Nesterov
- Bin Yu
- Shahar Mendelson
- E.J. Candes
- Robin Pemantle
- Mehryar Mohri
- Joel A. Tropp
- Roman Vershynin
- Razvan Pascanu
- B. Recht
Who trusts Max Simchowitz overall
- Samet Oymak
- Simon Shaolei Du
- Jason D. Lee
- Jakub Marecek
- Sarah Dean
- Pratik Chaudhari
- Qiuwei Li
- Jonas Köhler
- Leonard Adolphs
- M. Soltanolkotabi
Papers from Max Simchowitz's perspective
Ordered by how strongly the researchers Max Simchowitz 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
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Gradient Descent Converges to Minimizers (2016) — 218 citations
- Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification (2018) — 388 citations
- Low-rank Solutions of Linear Matrix Equations via Procrustes Flow (2015) — 420 citations
- Gradient Descent Only Converges to Minimizers (2016) — 646 citations