1 citations · 2 across the 7 of their papers we have counts for
7 papers
Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations
Mathieu Alain, So Takao, Xiaowen Dong +2
Predicting the labels of graph-structured data is crucial in scientific applications and is often achieved using graph neural networks (GNNs). However, when data is scarce, GNNs su…
The Manifold Density Function: An Intrinsic Method for the Validation of Manifold Learning
Benjamin Holmgren, Eli Quist, Jordan Schupbach +2
We introduce the manifold density function, which is an intrinsic method to validate manifold learning techniques. Our approach adapts and extends Ripley's -function, and catego…
Filtration Surfaces for Dynamic Graph Classification
Franz Srambical, Bastian Rieck
Existing approaches for classifying dynamic graphs either lift graph kernels to the temporal domain, or use graph neural networks (GNNs). However, current baselines have scalabilit…
Evaluating the "Learning on Graphs" Conference Experience
Bastian Rieck, Corinna Coupette
With machine learning conferences growing ever larger, and reviewing processes becoming increasingly elaborate, more data-driven insights into their workings are required. In this…
Metric Space Magnitude and Generalisation in Neural Networks
Rayna Andreeva, Katharina Limbeck, Bastian Rieck +1
Deep learning models have seen significant successes in numerous applications, but their inner workings remain elusive. The purpose of this work is to quantify the learning process…
DONUT -- Creation, Development, and Opportunities of a Database
Barbara Giunti, Jānis Lazovskis, Bastian Rieck
DONUT is a database of papers about practical, real-world uses of Topological Data Analysis (TDA). Its original seed was planted in a group chat formed during the HIM Spring School…