research
∙
06/23/2023
Topological signatures of periodic-like signals
We present a method to construct signatures of periodic-like data. Based...
share
research
∙
05/22/2023
MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Deep Neural Networks
Despite their successful application to a variety of tasks, neural netwo...
research
∙
05/28/2022
Topological phase estimation method for reparameterized periodic functions
We consider a signal composed of several periods of a periodic function,...
research
∙
02/03/2022
RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds
The use of topological descriptors in modern machine learning applicatio...
research
∙
05/07/2021
Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs
Although neural networks are capable of reaching astonishing performance...
research
∙
10/16/2020
Optimizing persistent homology based functions
Solving optimization tasks based on functions and losses with a topologi...
research
∙
02/07/2020
Efficient Topological Layer based on Persistent Landscapes
We propose a novel topological layer for general deep learning models ba...
research
∙
02/04/2020
Optimal quantization of the mean measure and application to clustering of measures
This paper addresses the case where data come as point sets, or more gen...
research
∙
10/14/2019
Quantitative stability of optimal transport maps and linearization of the 2-Wasserstein space
This work studies an explicit embedding of the set of probability measur...
research
∙
09/30/2019
ATOL: Measure Vectorisation for Automatic Topologically-Oriented Learning
Robust topological information commonly comes in the form of a set of pe...
research
∙
09/30/2019
ATOL: Automatic Topologically-Oriented Learning
There are abundant cases for using Topological Data Analysis (TDA) in a ...
research
∙
06/13/2019
Topological Data Analysis for Arrhythmia Detection through Modular Neural Networks
This paper presents an innovative and generic deep learning approach to ...
research
∙
04/20/2019
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
Persistence diagrams, the most common descriptors of Topological Data An...
research
∙
04/20/2019
PersLay: A Simple and Versatile Neural Network Layer for Persistence Diagrams
Persistence diagrams, a key descriptor from Topological Data Analysis, e...
research
∙
04/20/2019
A General Neural Network Architecture for Persistence Diagrams and Graph Classification
Graph classification is a difficult problem that has drawn a lot of atte...
research
∙
11/12/2018
DTM-based filtrations
Despite strong stability properties, the persistent homology of filtrati...
research
∙
02/28/2018
The density of expected persistence diagrams and its kernel based estimation
Persistence diagrams play a fundamental role in Topological Data Analysi...
research
∙
10/11/2017