Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine learning, neuroscience, signal processing, and statistics...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
The inverse problem with distributed dipoles models in M/EEG is strongly ill-posed requiring to set priors on the solution. Most common priors are based on a convenient ℓ2 norm....
Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statisti...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
—High instruction cache hit rates are key to high performance. One known technique to improve the hit rate of caches is to minimize cache interference by improving the layout of ...
A joint source-channel coding (JSCC) scheme for robust progressive image transmission over broadband wireless channels using orthogonal frequency division multiplexing (OFDM) syste...