research
∙
03/23/2023
Optimization Dynamics of Equivariant and Augmented Neural Networks
We investigate the optimization of multilayer perceptrons on symmetric d...
share
research
∙
10/20/2022
Bisparse Blind Deconvolution through Hierarchical Sparse Recovery
The bi-sparse blind deconvolution problem is studied – that is, from the...
research
∙
09/29/2022
In Search of Projectively Equivariant Neural Networks
Equivariance of linear neural network layers is well studied. In this wo...
research
∙
09/22/2022
One-Shot Messaging at Any Load Through Random Sub-Channeling in OFDM
Compressive Sensing has well boosted massive random access protocols ove...
research
∙
01/31/2022
Rigidity Preserving Image Transformations and Equivariance in Perspective
We characterize the class of image plane transformations which realize r...
research
∙
11/30/2021
ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds
In this paper, we are concerned with rotation equivariance on 2D point c...
research
∙
11/05/2021
Guaranteed blind deconvolution and demixing via hierarchically sparse reconstruction
The blind deconvolution problem amounts to reconstructing both a signal ...
research
∙
05/21/2021
Measure Concentration on the OFDM-based Random Access Channel
It is well known that CS can boost massive random access protocols. Usua...
research
∙
05/07/2021
Hierarchical sparse recovery from hierarchically structured measurements with application to massive random access
A new family of operators, coined hierarchical measurement operators, is...
research
∙
04/06/2021
Hierarchical compressed sensing
Compressed sensing is a paradigm within signal processing that provides ...
research
∙
05/20/2020
Hierarchical sparse recovery from hierarchically structured measurements
A new class of measurement operators for structured compressed sensing p...
research
∙
06/03/2018
Low-Overhead Hierarchically-Sparse Channel Estimation for Multiuser Wideband Massive MIMO
The problem of excessive pilot overhead required for uplink massive MIMO...
research
∙
03/29/2018
