Jacob Mattingley
Computational Mechanics · 2010–2011
A researcher in the Science corpus: 2 papers; trusts 29 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Computational Mechanics — 13 citations into it; #7,106 of 10,001 by trust here, #7,501 by citations
- Computational Theory and Mathematics — 8 citations into it
- Control and Systems Engineering — 7 citations into it; #5,621 of 8,257 by trust here, #5,784 by citations
- Computer Networks and Communications — 6 citations into it
- Computer Vision and Pattern Recognition — 6 citations into it; #15,547 of 21,575 by trust here, #18,266 by citations
- Numerical Analysis — 6 citations into it; #607 of 3,586 by trust here, #836 by citations
- Signal Processing — 6 citations into it
- Artificial Intelligence — 5 citations into it
Whom Jacob Mattingley trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Johan Efberg
- Stephen P. Boyd
- Stephen J. Wright
- E.J. Candes
- Vladimir Naumovich Vapni
- Antonin Chambolle
- Jan M. Maciejowski
- Johan Löfberg
- P. S. Ramirez
- Yang Wang
Who trusts Jacob Mattingley overall
- Stephen P. Boyd
- Panagiotis Patrinos
- Neal Parikh
- Craig Earl Beal
- Stephen M. Erlien
- J. Christian Gerdes
- Alexander Domahidi
- Seyed Sina Mirrazavi Salehian
- Alberto Bemporad
- Filipe de Avila Belbute-Peres
Papers from Jacob Mattingley's perspective
Ordered by how strongly the researchers Jacob Mattingley trusts endorse each paper. Same corpus, a different order for every researcher.
- Regression Shrinkage and Selection via the Lasso
- Atomic Decomposition by Basis Pursuit
- Decoding by linear programming
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- The Nature of Statistical Learning Theory
- Stable signal recovery from incomplete and inaccurate measurements
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Primal-Dual Interior-Point Methods
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Their papers
- CVXGEN: a code generator for embedded convex optimization (2011) — 880 citations
- Real-Time Convex Optimization in Signal Processing (2010) — 331 citations