157 citations · 242 across the 16 of their papers we have counts for
4 papers · 1 filter
Topological Regularization via Persistence-Sensitive Optimization
Arnur Nigmetov, Aditi S. Krishnapriyan, Nicole Sanderson +1
Optimization, a key tool in machine learning and statistics, relies on regularization to reduce overfitting. Traditional regularization methods control a norm of the solution to en…
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction
Nicolas Swenson, Aditi S. Krishnapriyan, Aydin Buluc +2
Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While…
Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks
Aditi S. Krishnapriyan, Joseph Montoya, Maciej Haranczyk +2
Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful…
Topological Descriptors Help Predict Guest Adsorption in Nanoporous Materials
Aditi S. Krishnapriyan, Maciej Haranczyk, Dmitriy Morozov
Machine learning has emerged as an attractive alternative to experiments and simulations for predicting material properties. Usually, such an approach relies on specific domain kno…