1 citations · 2 across the 4 of their papers we have counts for
4 papers
Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey
Rubén Ballester, Carles Casacuberta, Sergio Escalera
This survey provides a comprehensive exploration of applications of Topological Data Analysis (TDA) within neural network analysis. Using TDA tools such as persistent homology and…
Decorrelating neurons using persistence
Rubén Ballester, Carles Casacuberta, Sergio Escalera
We propose a novel way to improve the generalisation capacity of deep learning models by reducing high correlations between neurons. For this, we present two regularisation terms c…
A topological classifier to characterize brain states: When shape matters more than variance
Aina Ferrà, Gloria Cecchini, Fritz-Pere Nobbe Fisas +2
Despite the remarkable accuracies attained by machine learning classifiers to separate complex datasets in a supervised fashion, most of their operation falls short to provide an i…
Importance attribution in neural networks by means of persistence landscapes of time series
Aina Ferrà, Carles Casacuberta, Oriol Pujol
We propose and implement a method to analyze time series with a neural network using a matrix of area-normalized persistence landscapes obtained through topological data analysis.…