200 citations · 675 across the 45 of their papers we have counts for
4 papers · 1 filter
Semi-Supervised Clustering via Information-Theoretic Markov Chain Aggregation
Sophie Steger, Bernhard C. Geiger, Marek Smieja
We connect the problem of semi-supervised clustering to constrained Markov aggregation, i.e., the task of partitioning the state space of a Markov chain. We achieve this connection…
Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks
Franz M. Rohrhofer, Stefan Posch, Clemens Gößnitzer +1
Physics-informed neural networks (PINNs) have emerged as a promising deep learning method, capable of solving forward and inverse problems governed by differential equations. Despi…
Importance of feature engineering and database selection in a machine learning model: A case study on carbon crystal structures
Franz M. Rohrhofer, Santanu Saha, Simone Di Cataldo +3
Drive towards improved performance of machine learning models has led to the creation of complex features representing a database of condensed matter systems. The complex features,…
Synwalk -- Community Detection via Random Walk Modelling
Christian Toth, Denis Helic, Bernhard C. Geiger
Complex systems, abstractly represented as networks, are ubiquitous in everyday life. Analyzing and understanding these systems requires, among others, tools for community detectio…