research
∙
06/30/2023
ReLU Neural Networks, Polyhedral Decompositions, and Persistent Homolog
A ReLU neural network leads to a finite polyhedral decomposition of inpu...
share
research
∙
03/08/2022
The Flag Median and FlagIRLS
Finding prototypes (e.g., mean and median) for a dataset is central to a...
research
∙
10/21/2021
Supporting Massive DLRM Inference Through Software Defined Memory
Deep Learning Recommendation Models (DLRM) are widespread, account for a...
research
∙
11/30/2020
Locally Linear Attributes of ReLU Neural Networks
A ReLU neural network determines/is a continuous piecewise linear map fr...
research
∙
06/24/2020
The flag manifold as a tool for analyzing and comparing data sets
The shape and orientation of data clouds reflect variability in observat...
research
∙
06/27/2019
More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing
Compressive sensing (CS) is a method of sampling which permits some clas...
research
∙
06/20/2019
A data-driven approach to sampling matrix selection for compressive sensing
Sampling is a fundamental aspect of any implementation of compressive se...
research
∙
10/27/2018
Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large data sets
Dimensionality-reduction methods are a fundamental tool in the analysis ...
research
∙
08/05/2018
Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets
A fundamental question in many data analysis settings is the problem of ...
research
∙
07/10/2018
A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction
Dimensionality-reduction techniques are a fundamental tool for extractin...
research
∙
07/03/2018
Endmember Extraction on the Grassmannian
Endmember extraction plays a prominent role in a variety of data analysi...
research
∙
07/07/2016