1 citations · 1 across the 5 of their papers we have counts for
5 papers · 1 filter
High-Dimensional Sparse Bayesian Learning without Covariance Matrices
Alexander Lin, Andrew H. Song, Berkin Bilgic +1
Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem. However, the most popular inference algorithms for SBL become too expensive for high-…
Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution
Bahareh Tolooshams, Satish Mulleti, Demba Ba +1
We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution. In this problem, each channel's measurements are gi…
Fast Convolutional Dictionary Learning off the Grid
Andrew H. Song, Francisco J. Flores, Demba Ba
Given a continuous-time signal that can be modeled as the superposition of localized, time-shifted events from multiple sources, the goal of Convolutional Dictionary Learning (CDL)…
Sequential Detection of Regime Changes in Neural Data
Taposh Banerjee, Stephen Allsop, Kay M. Tye +2
The problem of detecting changes in firing patterns in neural data is studied. The problem is formulated as a quickest change detection problem. Important algorithms from the liter…
Deeply-Sparse Signal rePresentations ()
Demba Ba
A recent line of work shows that a deep neural network with ReLU nonlinearities arises from a finite sequence of cascaded sparse coding models, the outputs of which, except for the…