Nathan Halko

Computational Mechanics · 2009–2010

A researcher in the Science corpus: 2 papers; trusts 12 researchers and is trusted by 80 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 26 citations into it; #445 of 10,001 by trust here, #695 by citations
• Computational Theory and Mathematics — 14 citations into it
• Statistics and Probability — 12 citations into it
• Signal Processing — 11 citations into it
• Computational Mathematics — 9 citations into it; #25 of 612 by trust here, #31 by citations
• Computer Networks and Communications — 7 citations into it
• Numerical Methods & Scientific Computing — 5 citations into it

Whom Nathan Halko trusts overall

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

1. Petros Drineas
2. Gene H. Golub
3. Kenneth L. Clarkson
4. Sam T. Roweis
5. Michael W. Mahoney
6. Per-Gunnar Martinsson
7. J. Kuczy
8. E.J. Candes
9. Lars Grasedyck
10. Joel A. Tropp

Who trusts Nathan Halko overall

1. Joel A. Tropp
2. George C. Linderman
3. Per-Gunnar Martinsson
4. David P. Woodruff
5. Cameron Musco
6. Steven L. Brunton
7. Alex Gittens
8. Noureddine El Karoui
9. Long Chen
10. N. Benjamin Erichson

Papers from Nathan Halko's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Relative-Error CUR Matrix Decompositions (SIAM Journal on Matrix Analysis and Applications)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions (2009) — 3,165 citations
• An Algorithm for the Principal Component Analysis of Large Data Sets (2010) — 308 citations