Search Results for author:
Found 7 papers, 4 papers with code
MANTRA: The Manifold Triangulations Assemblage
The rising interest in leveraging higher-order interactions present in complex systems has led to a surge in more expressive models exploiting high-order structures in the data, especially in topological deep learning (TDL), which designs neural networks on high-order domains such as simplicial complexes.
Attending to Topological Spaces: The Cellular Transformer
Topological Deep Learning seeks to enhance the predictive performance of neural network models by harnessing topological structures in input data.
Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey
We discuss different strategies to obtain topological information from data and neural networks by means of TDA.
ICML 2023 Topological Deep Learning Challenge : Design and Results
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning.
Decorrelating neurons using persistence
We propose a novel way to improve the generalisation capacity of deep learning models by reducing high correlations between neurons.
On the Expressivity of Persistent Homology in Graph Learning
Persistent homology, a technique from computational topology, has recently shown strong empirical performance in the context of graph classification.
Predicting the generalization gap in neural networks using topological data analysis
Understanding how neural networks generalize on unseen data is crucial for designing more robust and reliable models.
