Sparse Approximation Accelerators with Spiking Neural-Networks
Sparse Recovery And Deep Learning For Extracting Time-Dependent Models From Data
Sparse matrix operations dominate the performance of many scientific and engineering applications. In particular, iterative methods are commonly used in algorithms for linear systems, least squares problems, and eigenvalue problems, which involve a sparse matrix-vector product in the inner loop. The performance of sparse matrix algorithms is often disappointing on modern machines because the algorithms have poor temporal and spatial locality, and are therefore limited by the speed of main memory.
Sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational cost. The approach has been successfully used in many areas, such as signal and image processing. In sparse learning classification, for example, the prediction accuracy or some other classical measure of performance is not the sole concern: we also wish to be able to better understand which few features are relevant as markers for classification.