Ashia C. Wilson
Artificial Intelligence · 2013–2017
A researcher in the Science corpus: 5 papers; trusts 25 researchers and is trusted by 75 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 41 citations into it; #3,270 of 35,345 by trust here, #4,080 by citations
- Optimization & Training — 35 citations into it; #754 of 2,601 by trust here, #571 by citations
- Computational Mechanics — 29 citations into it; #919 of 10,001 by trust here, #553 by citations
- Numerical Analysis — 19 citations into it; #632 of 3,586 by trust here, #1,007 by citations
- Mathematical Optimization — 15 citations into it; #308 of 422 by trust here, #292 by citations
- Computer Vision and Pattern Recognition — 9 citations into it
- Probabilistic & Bayesian Methods — 7 citations into it; #757 of 1,525 by trust here, #618 by citations
- Computational Theory and Mathematics — 6 citations into it; #8,457 of 12,182 by trust here, #10,901 by citations
Whom Ashia C. Wilson trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yurii E. Nesterov
- Michel Baes
- Weijie Su
- John Charles Butcher
- Geoffrey E. Hinton
- Diederik P. Kingma
- E.J. Candes
- David E. Rumelhart
- Ilya Sutskever
- Stephen P. Boyd
Who trusts Ashia C. Wilson overall
- Nicolas Loizou
- James Lucas
- Ilya Loshchilov
- Laurence Aitchison
- Nitish Shirish Keskar
- Richard S. Zemel
- Michael I. Jordan
- Rachel A. Ward
- Chi Jin
- Suriya Gunasekar
Papers from Ashia C. Wilson's perspective
Ordered by how strongly the researchers Ashia C. Wilson 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)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- 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
- The Marginal Value of Adaptive Gradient Methods in Machine Learning (2017) — 1,163 citations
- Streaming Variational Bayes (2013) — 368 citations
- A variational perspective on accelerated methods in optimization (2016) — 659 citations
- A Lyapunov Analysis of Momentum Methods in Optimization (2016) — 236 citations
- On Accelerated Methods in Optimization (2015) — 30 citations