Sharan Vaswani
Optimization & Training · 2018–2020
A researcher in the Science corpus: 4 papers; trusts 204 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Optimization & Training — 216 citations into it; #737 of 2,601 by trust here, #1,046 by citations
- Artificial Intelligence — 167 citations into it; #7,880 of 35,345 by trust here, #12,974 by citations
- Computational Mechanics — 113 citations into it
- Management Science and Operations Research — 39 citations into it
- Statistics and Probability — 32 citations into it
- Computer Vision and Pattern Recognition — 29 citations into it
- Numerical Analysis — 27 citations into it
- Computational Theory and Mathematics — 24 citations into it
Whom Sharan Vaswani trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yurii E. Nesterov
- Mark W. Schmidt
- Diederik P. Kingma
- Yoshua Bengio
- Geoffrey E. Hinton
- Francis R. Bach
- Ilya Sutskever
- Kaiming He
- Alex Krizhevsky
- Boris Polyak
Who trusts Sharan Vaswani overall
- Nicolas Loizou
- Robert Mansel Gower
- Chaoyue Liu
- Issam Hadj Laradji
- Ryan D'Orazio
- S. Meng
- Simon Lacoste-Julien
- Leonard Berrada
- Mahmoud Assran
- Konstantin Mishchenko
Papers from Sharan Vaswani's perspective
Ordered by how strongly the researchers Sharan Vaswani 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)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron (2018) — 345 citations
- Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence (2020) — 242 citations
- Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation (2019) — 40 citations
- Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates (2019) — 247 citations