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Found 10 papers, 5 papers with code
Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds
Machine learning for point clouds has been attracting much attention, with many applications in various fields, such as shape recognition and material science.
MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Deep Neural Networks
Despite their successful application to a variety of tasks, neural networks remain limited, like other machine learning methods, by their sensitivity to shifts in the data: their performance can be severely impacted by differences in distribution between the data on which they were trained and that on which they are deployed.
Counterfactual Explanation with Missing Values
Then, we propose a new framework of CE, named Counterfactual Explanation by Pairs of Imputation and Action (CEPIA), that enables users to obtain valid actions even with missing values and clarifies how actions are affected by imputation of the missing values.
Vanishing Component Analysis with Contrastive Normalization
Vanishing component analysis (VCA) computes approximate generators of vanishing ideals of samples, which are further used for extracting nonlinear features of the samples.
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 applications, such as Persistence Diagrams (PDs) arising from Topological Data Analysis (TDA), has shown great potential in various domains.
Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs
We showcase experimentally the potential of Topological Uncertainty in the context of trained network selection, Out-Of-Distribution detection, and shift-detection, both on synthetic and real datasets of images and graphs.
Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization
One of the popular methods is Counterfactual Explanation (CE), also known as Actionable Recourse, which provides a user with a perturbation vector of features that alters the prediction result.
Application of Topological Data Analysis to Delirium Detection
We propose a new scoring algorithm for detecting delirium from one-channel EEG, based on topological data analysis.
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
Persistence diagrams, the most common descriptors of Topological Data Analysis, encode topological properties of data and have already proved pivotal in many different applications of data science.
DTM-based Filtrations
Despite strong stability properties, the persistent homology of filtrations classically used in Topological Data Analysis, such as, e. g. the Cech or Vietoris-Rips filtrations, are very sensitive to the presence of outliers in the data from which they are computed.
Computational Geometry Algebraic Topology
