Alfred Marcel Bruckstein
Computer Vision and Pattern Recognition · 1996–2010
A researcher in the Science corpus: 8 papers; trusts 61 researchers and is trusted by 224 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 90 citations into it; #3,971 of 21,575 by trust here, #4,469 by citations
- Computational Mechanics — 73 citations into it; #90 of 10,001 by trust here, #106 by citations
- Signal Processing — 44 citations into it; #206 of 7,572 by trust here, #248 by citations
- Media Technology — 25 citations into it; #811 of 3,621 by trust here, #1,046 by citations
- Applied Mathematics — 21 citations into it; #891 of 7,620 by trust here, #562 by citations
- Representation & Self-Supervised Learning — 17 citations into it
- Biomedical Engineering — 12 citations into it
- Artificial Intelligence — 11 citations into it
Whom Alfred Marcel Bruckstein trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David L. Donoho
- Michael Elad
- E.J. Candes
- Stéphane Mallat
- Joel A. Tropp
- Michal Aharon
- Jean-Luc Starck
- Bhaskar D. Rao
- Patrik O. Hoyer
- Scott Shaobing Chen
Who trusts Alfred Marcel Bruckstein overall
- Michael Elad
- Lei Zhang
- Julien Mairal
- Weisheng Dong
- Ron Rubinstein
- Xiaojun Chen
- E.J. Candes
- Marian-Daniel Iordache
- Joel A. Tropp
- Brendt E. Wohlberg
Papers from Alfred Marcel Bruckstein's perspective
Ordered by how strongly the researchers Alfred Marcel Bruckstein trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Atomic Decomposition by Basis Pursuit
- Emergence of simple-cell receptive field properties by learning a sparse code for natural images
- Regression Shrinkage and Selection via the Lasso
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images (2009) — 2,385 citations
- Dictionaries for Sparse Representation Modeling (2010) — 1,356 citations
- Partial Similarity of Objects, or How to Compare a Centaur to a Horse (2009) — 117 citations
- A generalized uncertainty principle and sparse representation in pairs of bases (2002) — 668 citations
- On the Uniqueness of Nonnegative Sparse Solutions to Underdetermined Systems of Equations (2008) — 311 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
- Sub-pixel distance maps and weighted distance transforms (1996)