10 citations · 29 across the 9 of their papers we have counts for
9 papers
Singularities in Bayesian Inference: Crucial or Overstated?
Maria De Iorio, Andreas Heinecke, Beatrice Franzolini +1
Over the past two decades, shrinkage priors have become increasingly popular, and many proposals can be found in the literature. These priors aim to shrink small effects to zero wh…
RIP sensing matrices construction for sparsifying dictionaries with application to MRI imaging
Jinn Ho, Wen-Liang Hwang, Andreas Heinecke
Practical applications of compressed sensing often restrict the choice of its two main ingredients. They may (i) prescribe using particular redundant dictionaries for certain class…
Operation-based Collaborative Data Sharing for Distributed Systems
Masato Takeichi
Collaborative Data Sharing raises a fundamental issue in distributed systems. Several strategies have been proposed for making shared data consistent between peers in such a way th…
Cohesion and Repulsion in Bayesian Distance Clustering
Abhinav Natarajan, Maria De Iorio, Andreas Heinecke +2
Clustering in high-dimensions poses many statistical challenges. While traditional distance-based clustering methods are computationally feasible, they lack probabilistic interpret…
Deep Representation with ReLU Neural Networks
Andreas Heinecke, Wen-Liang Hwang
We consider deep feedforward neural networks with rectified linear units from a signal processing perspective. In this view, such representations mark the transition from using a s…
Necessary and sufficient conditions to perform Spectral Tetris
Peter Casazza, Andreas Heinecke, Keri Kornelson +2
Spectral Tetris has proved to be a powerful tool for constructing sparse equal norm Hilbert space frames. We introduce a new form of Spectral Tetris which works for non-equal norm…