Robert Mansel Gower
Optimization & Training · 2015–2021
A researcher in the Science corpus: 10 papers; trusts 136 researchers and is trusted by 51 researchers here, as inferred from the citation graph.
Subfields
- Optimization & Training — 200 citations into it; #547 of 2,601 by trust here, #861 by citations
- Artificial Intelligence — 191 citations into it; #2,735 of 35,345 by trust here, #5,749 by citations
- Computational Mechanics — 136 citations into it; #763 of 10,001 by trust here, #1,651 by citations
- Computational Theory and Mathematics — 43 citations into it; #3,266 of 12,182 by trust here, #6,798 by citations
- Statistics and Probability — 43 citations into it; #1,602 of 12,932 by trust here, #2,629 by citations
- Management Science and Operations Research — 31 citations into it; #3,599 of 11,946 by trust here, #2,957 by citations
- Computer Vision and Pattern Recognition — 27 citations into it; #6,617 of 21,575 by trust here, #13,269 by citations
- Numerical Analysis — 23 citations into it; #437 of 3,586 by trust here, #875 by citations
Whom Robert Mansel Gower trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Peter Richtárik
- Shai Shalev-Shwartz
- Yurii E. Nesterov
- Tong Zhang
- Chih-Jen Lin
- Donald Goldfarb
- Jorge Nocedal
- Francis R. Bach
- Martin Takác
- David F. Shanno
Who trusts Robert Mansel Gower overall
- Nicolas Loizou
- Peter Richtárik
- Filip Hanzely
- Jakub Konecný
- Ahmed Khaled
- Martin Takác
- Sebastian U. Stich
- Samuel Horváth
- Konstantin Mishchenko
- Othmane Sebbouh
Papers from Robert Mansel Gower's perspective
Ordered by how strongly the researchers Robert Mansel Gower 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Stochastic Block BFGS: Squeezing More Curvature out of Data (2016) — 169 citations
- SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation (2020) — 104 citations
- Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization (2018) — 49 citations
- Randomized Quasi-Newton Updates Are Linearly Convergent Matrix Inversion Algorithms (2016) — 87 citations
- Stochastic quasi-gradient methods: variance reduction via Jacobian sketching (2018) — 115 citations
- Randomized Iterative Methods for Linear Systems (2015) — 327 citations
- Towards closing the gap between the theory and practice of SVRG (2019) — 17 citations
- SGD: General Analysis and Improved Rates (2019) — 484 citations
- Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization (2020) — 58 citations
- Stochastic Polyak Stepsize with a Moving Target (2021) — 21 citations