102 citations · 261 across the 23 of their papers we have counts for
5 papers · 1 filter
Efficient Inter-Geodesic Distance Computation and Fast Classical Scaling
Gil Shamai, Michael Zibulevsky, Ron Kimmel
Multidimensional scaling (MDS) is a dimensionality reduction tool used for information analysis, data visualization and manifold learning. Most MDS procedures embed data points in…
Compressed Learning: A Deep Neural Network Approach
Amir Adler, Michael Elad, Michael Zibulevsky
Compressed Learning (CL) is a joint signal processing and machine learning framework for inference from a signal, using a small number of measurements obtained by linear projection…
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
Elad Richardson, Rom Herskovitz, Boris Ginsburg +1
We present SEBOOST, a technique for boosting the performance of existing stochastic optimization methods. SEBOOST applies a secondary optimization process in the subspace spanned b…
A Deep Learning Approach to Block-based Compressed Sensing of Images
Amir Adler, David Boublil, Michael Elad +1
Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal.…
Trainlets: Dictionary Learning in High Dimensions
Jeremias Sulam, Boaz Ophir, Michael Zibulevsky +1
Sparse representations has shown to be a very powerful model for real world signals, and has enabled the development of applications with notable performance. Combined with the abi…