Malik Magdon-Ismail

Computational Mechanics · 1998–2011

A researcher in the Science corpus: 4 papers; trusts 8 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 12 citations into it; #1,756 of 10,001 by trust here, #2,644 by citations
• Artificial Intelligence — 7 citations into it; #8,036 of 35,345 by trust here, #13,962 by citations
• Computational Theory and Mathematics — 7 citations into it; #6,157 of 12,182 by trust here, #7,501 by citations
• Signal Processing — 7 citations into it
• Computational Mathematics — 5 citations into it; #162 of 612 by trust here, #191 by citations

Whom Malik Magdon-Ismail trusts overall

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

1. Michael W. Mahoney
2. Petros Drineas
3. Nir Ailon
4. Joel A. Tropp
5. Tamás Sarlós
6. Gene H. Golub
7. S. Muthu Muthukrishnan
8. Bernard Chazelle
9. Nathan Halko
10. Vladimir Rokhlin

Who trusts Malik Magdon-Ismail overall

1. Yu-Xiang Wang
2. Michael W. Mahoney
3. David P. Woodruff
4. Elad Amrani
5. Alessandro Rudi
6. Tim Kraska
7. Amit Deshpande
8. Alex Kulesza
9. A. Alaoui
10. Kenneth L. Clarkson

Papers from Malik Magdon-Ismail's perspective

Ordered by how strongly the researchers Malik Magdon-Ismail trusts endorse each paper. Same corpus, a different order for every researcher.

1. Relative-Error CUR Matrix Decompositions (SIAM Journal on Matrix Analysis and Applications)
2. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions
3. Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
4. Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication
5. Greed is good: algorithmic results for sparse approximation (IEEE Transactions on Information Theory)
6. ImageNet classification with deep convolutional neural networks
7. CUR matrix decompositions for improved data analysis
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. Gradient-based learning applied to document recognition
10. The Fast Johnson--Lindenstrauss Transform and Approximate Nearest Neighbors

Their papers

• On selecting a maximum volume sub-matrix of a matrix and related problems (2009) — 251 citations
• Fast approximation of matrix coherence and statistical leverage (2011) — 583 citations
• On the maximum drawdown of a Brownian motion (2004) — 120 citations
• Neural Networks for Density Estimation (1998)