Julie Nutini
Computational Mechanics · 2015–2017
A researcher in the Science corpus: 3 papers; trusts 58 researchers and is trusted by 31 researchers here, as inferred from the citation graph.
Subfields
- Computational Mechanics — 57 citations into it; #1,429 of 10,001 by trust here, #1,219 by citations
- Artificial Intelligence — 53 citations into it; #6,868 of 35,345 by trust here, #8,439 by citations
- Optimization & Training — 33 citations into it; #741 of 2,601 by trust here, #861 by citations
- Numerical Analysis — 28 citations into it
- Computational Theory and Mathematics — 23 citations into it
- Statistics and Probability — 15 citations into it
- Mathematical Optimization — 10 citations into it
- Computer Vision and Pattern Recognition — 9 citations into it
Whom Julie Nutini trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Peter Richtárik
- Yurii E. Nesterov
- Paul Tseng
- Martin Takác
- Dimitri P. Bertsekas
- Saharon Rosset
- Amir Beck
- Olivier Fercoq
- Zheng Qu
- Francis R. Bach
Who trusts Julie Nutini overall
- Nicolas Loizou
- Sebastian U. Stich
- Issam Hadj Laradji
- Sharan Vaswani
- Mark W. Schmidt
- Farzin Haddadpour
- Anant Raj
- Mingyi Hong
- Martin Jaggi
- Robert Mansel Gower
Papers from Julie Nutini's perspective
Ordered by how strongly the researchers Julie Nutini trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Regression Shrinkage and Selection via the Lasso
Their papers
- Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition (2016) — 1,544 citations
- Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection (2015) — 249 citations
- Let's Make Block Coordinate Descent Go Fast: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence (2017) — 50 citations