Michael P. Friedlander
Artificial Intelligence · 2008–2016
A researcher in the Science corpus: 6 papers; trusts 48 researchers and is trusted by 140 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 37 citations into it; #3,034 of 35,345 by trust here, #6,720 by citations
- Computational Mechanics — 31 citations into it; #195 of 10,001 by trust here, #617 by citations
- Optimization & Training — 24 citations into it; #364 of 2,601 by trust here, #692 by citations
- Statistics and Probability — 13 citations into it
- Computer Vision and Pattern Recognition — 12 citations into it
- Numerical Analysis — 12 citations into it
- Computational Theory and Mathematics — 10 citations into it
- Natural Language Processing — 8 citations into it
Whom Michael P. Friedlander trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yurii E. Nesterov
- Dimitri P. Bertsekas
- Radford M. Neal
- Arkadi Nemirovski
- Lawrence R. Rabiner
- Robert Malouf
- Hanna M. Wallach
- Kevin P. Murphy
- Fei Sha
- E. Tjong Kim Sang
Who trusts Michael P. Friedlander overall
- Mark W. Schmidt
- Nicolas Loizou
- Soham De
- Jakub Konecný
- Fabian Pedregosa
- Peter Richtárik
- Farbod Roosta-Khorasani
- Nicolas Le Roux
- Sharan Vaswani
- Tal Ben-Nun
Papers from Michael P. Friedlander's perspective
Ordered by how strongly the researchers Michael P. Friedlander trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Regression Shrinkage and Selection via the Lasso
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Satisfying Real-world Goals with Dataset Constraints (2016) — 218 citations
- Hybrid Deterministic-Stochastic Methods for Data Fitting (2011) — 414 citations
- Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection (2015) — 249 citations
- A Hybrid Stochastic-deterministic Optimization Method for Waveform Inversion (2011) — 7 citations
- Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm (2009) — 283 citations
- Probing the Pareto Frontier for Basis Pursuit Solutions (2008) — 2,086 citations