Pinned Repositories
1003834905
Config files for my GitHub profile.
DICTOL
DICTOL - A Dictionary Learning Toolbox in Matlab and Python
Reproducible-Deep-Compressive-Sensing
Collection of reproducible deep learning for compressive sensing
Residual-Ratio-Thresholding
Residual Ratio Thresholding (RRT) is a technique to identify the the correct regression model from a sequence of nested models. This has found applications in sparse linear regression aka compressed sensing and robust regression with sparse outliers. RRT allows the operation of algorithms like LASSO, OMP and their derivatives without knowing signal sparsity and noise variance. RRT also provides easy to interpret final sample support recovery guarantees. RRT is closely related to various information theoretic criteria. However, many of these information theoretic criteria, RRT is based on finite sample distributional results.
variational-bayes-cs
Scalable sparse Bayesian learning for large CS recovery problems
1003834905's Repositories
1003834905/1003834905
Config files for my GitHub profile.
1003834905/DICTOL
DICTOL - A Dictionary Learning Toolbox in Matlab and Python
1003834905/Reproducible-Deep-Compressive-Sensing
Collection of reproducible deep learning for compressive sensing
1003834905/Residual-Ratio-Thresholding
Residual Ratio Thresholding (RRT) is a technique to identify the the correct regression model from a sequence of nested models. This has found applications in sparse linear regression aka compressed sensing and robust regression with sparse outliers. RRT allows the operation of algorithms like LASSO, OMP and their derivatives without knowing signal sparsity and noise variance. RRT also provides easy to interpret final sample support recovery guarantees. RRT is closely related to various information theoretic criteria. However, many of these information theoretic criteria, RRT is based on finite sample distributional results.
1003834905/variational-bayes-cs
Scalable sparse Bayesian learning for large CS recovery problems