Dror Baron
Computational Mechanics · 2005–2009
A researcher in the Science corpus: 9 papers; trusts 26 researchers and is trusted by 27 researchers here, as inferred from the citation graph.
Subfields
- Computational Mechanics — 58 citations into it; #1,343 of 10,001 by trust here, #584 by citations
- Computer Vision and Pattern Recognition — 32 citations into it; #16,788 of 21,575 by trust here, #13,269 by citations
- Computer Networks and Communications — 25 citations into it; #2,152 of 9,886 by trust here, #1,539 by citations
- Signal Processing — 21 citations into it; #3,308 of 7,572 by trust here, #4,373 by citations
- Applied Mathematics — 19 citations into it
- Biomedical Engineering — 17 citations into it; #1,067 of 10,734 by trust here, #1,861 by citations
- Electrical and Electronic Engineering — 14 citations into it
- Artificial Intelligence — 11 citations into it
Whom Dror Baron trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David L. Donoho
- E.J. Candes
- Joel A. Tropp
- Scott Shaobing Chen
- David John Cameron MacKay
- Judea Pearl
- Terence Tao
- Finn Verner Jensen
- Jean-Jacques Fuchs
- Justin K. Romberg
Who trusts Dror Baron overall
- Richard Baraniuk
- Marco F. Duarte
- Florent Krzakala
- Lenka Zdeborov'a
- David L. Donoho
- Xiongbin Rao
- Yonina C. Eldar
- Ashish Kapoor
- Moshe Mishali
- Danny Bickson
Papers from Dror Baron's perspective
Ordered by how strongly the researchers Dror Baron trusts endorse each paper. Same corpus, a different order for every researcher.
- Regression Shrinkage and Selection via the Lasso
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Atomic Decomposition by Basis Pursuit
- 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)
- GENERATIVE ADVERSARIAL NETS
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Analog-to-Information Conversion via Random Demodulation (2006) — 428 citations
- A new compressive imaging camera architecture using optical-domain compression (2006) — 641 citations
- Distributed Compressive Sensing (2009) — 568 citations
- Random Filters for Compressive Sampling and Reconstruction (2006) — 317 citations
- An Architecture for Compressive Imaging (2006) — 373 citations
- Distributed Compressed Sensing of Jointly Sparse Signals (2005) — 596 citations
- The secrecy of compressed sensing measurements (2008) — 350 citations
- Fault Identification Via Nonparametric Belief Propagation (2009) — 21 citations
- Bayesian Compressive Sensing Via Belief Propagation (2008) — 529 citations