Eduard A. Gorbunov
Artificial Intelligence · 2019
A researcher in the Science corpus: 2 papers; trusts 26 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 46 citations into it; #11,840 of 35,345 by trust here, #21,878 by citations
- Optimization & Training — 41 citations into it; #1,482 of 2,601 by trust here, #2,112 by citations
- Computational Mechanics — 24 citations into it; #2,894 of 10,001 by trust here, #5,157 by citations
- Statistics and Probability — 13 citations into it; #6,008 of 12,932 by trust here, #7,508 by citations
- Computational Theory and Mathematics — 8 citations into it
- Efficiency & Compression — 8 citations into it
- Computer Vision and Pattern Recognition — 7 citations into it
- Management Science and Operations Research — 5 citations into it
Whom Eduard A. Gorbunov trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Peter Richtárik
- Yurii E. Nesterov
- Robert Mansel Gower
- Martin Takác
- Chih-Jen Lin
- Shai Shalev-Shwartz
- Francis R. Bach
- Tong Zhang
- Jakub Konecný
- Aaron Defazio
Who trusts Eduard A. Gorbunov overall
- Ahmed Khaled
- Peter Richtárik
- Filip Hanzely
- Nicolas Loizou
- Samuel Horváth
- Othmane Sebbouh
- Laurent Condat
- S. Chraibi
- Hugo Berard
- Dmitry Kovalev
Papers from Eduard A. Gorbunov's perspective
Ordered by how strongly the researchers Eduard A. Gorbunov trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent (2019) — 171 citations
- Distributed learning with compressed gradient differences (2019) — 208 citations