Ahmed Khaled
Artificial Intelligence · 2019–2020
A researcher in the Science corpus: 8 papers; trusts 205 researchers and is trusted by 39 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 180 citations into it; #5,329 of 35,345 by trust here, #9,465 by citations
- Optimization & Training — 151 citations into it; #528 of 2,601 by trust here, #843 by citations
- Computational Mechanics — 84 citations into it; #2,888 of 10,001 by trust here, #3,190 by citations
- Federated & Distributed Learning — 49 citations into it; #48 of 319 by trust here, #137 by citations
- Efficiency & Compression — 35 citations into it
- Computer Vision and Pattern Recognition — 32 citations into it
- Computer Networks and Communications — 31 citations into it
- ML Systems & Hardware — 31 citations into it; #370 of 575 by trust here, #549 by citations
Whom Ahmed Khaled trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Peter Richtárik
- Sebastian U. Stich
- Chih-Jen Lin
- Konstantin Mishchenko
- Yurii E. Nesterov
- Jakub Konecný
- Robert Mansel Gower
- Shai Shalev-Shwartz
- Tong Zhang
- Francis R. Bach
Who trusts Ahmed Khaled overall
- Grigory Malinovsky
- Blake E. Woodworth
- Peter Richtárik
- Dmitry Kovalev
- Sai Praneeth Karimireddy
- Nicolas Loizou
- Filip Hanzely
- Farzin Haddadpour
- Robert Mansel Gower
- Vivak Patel
Papers from Ahmed Khaled's perspective
Ordered by how strongly the researchers Ahmed Khaled 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
- 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
- First Analysis of Local GD on Heterogeneous Data (2019) — 180 citations
- Tighter Theory for Local SGD on Identical and Heterogeneous Data (2019) — 489 citations
- Gradient Descent with Compressed Iterates (2019) — 26 citations
- Better Theory for SGD in the Nonconvex World (2020) — 242 citations
- Random Reshuffling: Simple Analysis with Vast Improvements (2020) — 164 citations
- Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization (2020) — 58 citations
- Distributed Fixed Point Methods with Compressed Iterates (2019) — 18 citations
- Better Communication Complexity for Local SGD (2019) — 30 citations