Michal Aharon

Computer Vision and Pattern Recognition · 2005–2006

A researcher in the Science corpus: 4 papers; trusts 63 researchers and is trusted by 1,273 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 39 citations into it; #1,202 of 21,575 by trust here, #2,836 by citations
• Computational Mechanics — 22 citations into it; #56 of 10,001 by trust here, #98 by citations
• Signal Processing — 22 citations into it; #205 of 7,572 by trust here, #248 by citations
• Representation & Self-Supervised Learning — 11 citations into it
• Applied Mathematics — 8 citations into it
• Media Technology — 8 citations into it; #54 of 3,621 by trust here, #140 by citations
• Optimization & Training — 7 citations into it
• Artificial Intelligence — 6 citations into it

Whom Michal Aharon trusts overall

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

1. David L. Donoho
2. Bhaskar D. Rao
3. Patrik O. Hoyer
4. Barbara Olshausen
5. Michael Elad
6. Kenneth Kreutz-Delgado
7. Joel A. Tropp
8. Michael S. Lewicki
9. Terrence J. Sejnowski
10. Daniel D. Lee

Who trusts Michal Aharon overall

1. Julien Mairal
2. Michael Elad
3. Lei Zhang
4. Weisheng Dong
5. Francis R. Bach
6. Ron Rubinstein
7. Guillermo Sapiro
8. Meng Yang
9. Sanjeev Arora
10. Liefeng Bo

Papers from Michal Aharon's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. ImageNet: A large-scale hierarchical image database
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. Attention is All you Need (Neural Information Processing Systems)
9. Sparse coding with an overcomplete basis set: a strategy employed by V1?
10. Atomic Decomposition by Basis Pursuit

Their papers

• Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries (2006) — 5,637 citations
• Image Denoising Via Learned Dictionaries and Sparse representation (2006) — 451 citations
• $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation (2006) — 8,032 citations
• K-SVD and its non-negative variant for dictionary design (2005) — 182 citations