Search Results for author:
Found 13 papers, 3 papers with code
Quantum Distance Approximation for Persistence Diagrams
Topological Data Analysis methods can be useful for classification and clustering tasks in many different fields as they can provide two dimensional persistence diagrams that summarize important information about the shape of potentially complex and high dimensional data sets.
Position Paper: Challenges and Opportunities in Topological Deep Learning
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models.
A Topological Deep Learning Framework for Neural Spike Decoding
Brains use head direction cells to determine orientation whereas grid cells consist of layers of decked neurons that overlay to provide environment-based navigation.
Quantum Persistent Homology for Time Series
Persistent homology, a powerful mathematical tool for data analysis, summarizes the shape of data through tracking topological features across changes in different scales.
A Random Persistence Diagram Generator
Topological data analysis (TDA) studies the shape patterns of data.
Simplicial Complex Representation Learning
In this work, we propose a method for simplicial complex-level representation learning that embeds a simplicial complex to a universal embedding space in a way that complex-to-complex proximity is preserved.
Materials Fingerprinting Classification
Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates.
Topological Deep Learning
This work introduces the Topological CNN (TCNN), which encompasses several topologically defined convolutional methods.
Topological Convolutional Neural Networks
There is considerable interest in making convolutional neural networks (CNNs) that learn on less data, are better at generalizing, and are more easily interpreted.
Bayesian Topological Learning for Classifying the Structure of Biological Networks
In this work, we analyze and classify these filament networks by transforming them into persistence diagrams whose variability is quantified via a Bayesian framework on the space of persistence diagrams.
Bayesian Topological Learning for Brain State Classification
Investigation of human brain states through electroencephalograph (EEG) signals is a crucial step in human-machine communications.
A Bayesian Framework for Persistent Homology
In essence, we model persistence diagrams as Poisson point processes with prior intensities and compute posterior intensities by adopting techniques from the theory of marked point processes.
Methodology 62F15, 60G55, 62-07
A Stable Cardinality Distance for Topological Classification
This work incorporates topological features via persistence diagrams to classify point cloud data arising from materials science.
