Significant technological advances made in recent years have shepherded a dramatic
increase in utilization of digital technologies for biomedicine–everything from the …

In topological data analysis and visualization, topological descriptors such as persistence
diagrams, merge trees, contour trees, Reeb graphs, and Morse–Smale complexes play an …

Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex
data sets based on ideas a branch of mathematics called algebraic topology. TDA has …

Transformer-based language models such as BERT and its variants have found widespread
use in natural language processing (NLP). A common way of using these models is to fine …

Topological data analysis (TDA) is a branch of computational mathematics, bridging
algebraic topology and data science, that provides compact, noise-robust representations of …

Deep neural networks such as GoogLeNet, ResNet, and BERT have achieved impressive
performance in tasks such as image and text classification. To understand how such …

The mapper construction is a widely used tool from topological data analysis in obtaining
topological summaries of large, high-dimensional point cloud data. It has enjoyed great …

For better translational outcomes, researchers and clinicians alike demand novel tools to
distill complex neuroimaging data into simple yet behaviorally relevant representations at …

The Mapper algorithm is a visualization technique in topological data analysis (TDA) that
outputs a graph reflecting the structure of a given dataset. The Mapper algorithm requires …

Deep models are known to be vulnerable to data adversarial attacks, and many adversarial
training techniques have been developed to improve their adversarial robustness. While …