Hayden Schaeffer

Computational Mechanics · 2012–2017

A researcher in the Science corpus: 3 papers; trusts 24 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 25 citations into it; #664 of 10,001 by trust here, #2,112 by citations
• Computer Vision and Pattern Recognition — 19 citations into it
• Mathematical Physics — 9 citations into it; #4,546 of 8,562 by trust here, #3,828 by citations
• Optimization & Training — 7 citations into it
• Applied Mathematics — 6 citations into it
• Biomedical Engineering — 6 citations into it
• Numerical Methods & Scientific Computing — 6 citations into it
• Statistical and Nonlinear Physics — 5 citations into it; #476 of 10,127 by trust here, #1,990 by citations

Whom Hayden Schaeffer trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Robert Tibshirani
2. Rick Chartrand
3. David L. Donoho
4. Patrick L. Combettes
5. E.J. Candes
6. Steven L. Brunton
7. Richard Baraniuk
8. Jean-Christophe Pesquet
9. Yurii E. Nesterov
10. Yves Meyer

Who trusts Hayden Schaeffer overall

1. Steven L. Brunton
2. J. Nathan Kutz
3. Jens Berg
4. Zhao Chen
5. Maizar Raissi
6. Jean-Christophe Loiseau
7. Daniel A. Messenger
8. Joshua L. Proctor
9. Zichao Long
10. Kadierdan Kaheman

Papers from Hayden Schaeffer's perspective

Ordered by how strongly the researchers Hayden Schaeffer trusts endorse each paper. Same corpus, a different order for every researcher.

1. Regression Shrinkage and Selection via the Lasso
2. Atomic Decomposition by Basis Pursuit
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Nonlinear total variation based noise removal algorithms
7. Gradient-based learning applied to document recognition
8. Compressed Sensing
9. Stable signal recovery from incomplete and inaccurate measurements
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• On the Compressive Spectral Method (2014) — 57 citations
• Learning partial differential equations via data discovery and sparse optimization (2017) — 531 citations
• Sparse dynamics for partial differential equations (2012) — 190 citations