Nicolas Loizou
Artificial Intelligence · 2016–2022
A researcher in the Science corpus: 16 papers; trusts 521 researchers and is trusted by 41 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 483 citations into it; #3,561 of 35,345 by trust here, #4,797 by citations
- Optimization & Training — 462 citations into it; #429 of 2,601 by trust here, #479 by citations
- Computational Mechanics — 301 citations into it; #887 of 10,001 by trust here, #1,929 by citations
- Computer Networks and Communications — 103 citations into it; #2,358 of 9,886 by trust here, #1,539 by citations
- Computational Theory and Mathematics — 96 citations into it
- Statistics and Probability — 92 citations into it
- Numerical Analysis — 81 citations into it; #1,506 of 3,586 by trust here, #2,107 by citations
- Management Science and Operations Research — 78 citations into it; #1,292 of 11,946 by trust here, #1,590 by citations
Whom Nicolas Loizou trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Peter Richtárik
- Robert Mansel Gower
- Yurii E. Nesterov
- Mark W. Schmidt
- Deanna Needell
- Aaron Defazio
- Martin Takác
- Francis R. Bach
- Ilya Sutskever
- Chih-Jen Lin
Who trusts Nicolas Loizou overall
- Peter Richtárik
- Robert Mansel Gower
- Simon Lacoste-Julien
- Sharan Vaswani
- Hugo Berard
- Filip Hanzely
- Martin Takác
- Jianyu Wang
- Issam Hadj Laradji
- Antonio Orvieto
Papers from Nicolas Loizou's perspective
Ordered by how strongly the researchers Nicolas Loizou 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
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation (2020) — 104 citations
- Stochastic Gradient Push for Distributed Deep Learning (2018) — 410 citations
- Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods (2017) — 236 citations
- A Unified Theory of Decentralized SGD with Changing Topology and Local Updates (2020) — 652 citations
- Linearly convergent stochastic heavy ball method for minimizing generalization error (2017) — 47 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 Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize (2021) — 42 citations
- Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution (2022) — 53 citations
- Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity (2021) — 64 citations
- Stochastic Hamiltonian Gradient Methods for Smooth Games (2020) — 55 citations
- Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence (2020) — 242 citations
- Convergence Analysis of Inexact Randomized Iterative Methods (2019) — 23 citations
- Randomized Iterative Methods for Linear Systems: Momentum, Inexactness and Gossip (2019) — 5 citations
- Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols (2019) — 42 citations
- A new perspective on randomized gossip algorithms (2016) — 32 citations