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Found 11 papers, 7 papers with code
Ordered Topological Deep Learning: a Network Modeling Case Study
Computer networks are the foundation of modern digital infrastructure, facilitating global communication and data exchange.
TopoTune : A Framework for Generalized Combinatorial Complex Neural Networks
Combinatorial Complex Neural Networks (CCNNs), fairly general TDL models, have been shown to be more expressive and better performing than GNNs.
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM).
Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures
Echoing the 19th-century revolutions that gave rise to non-Euclidean geometry, an emerging line of research is redefining modern machine learning with non-Euclidean structures.
TopoBench: A Framework for Benchmarking Topological Deep Learning
This work introduces TopoBench, an open-source library designed to standardize benchmarking and accelerate research in topological deep learning (TDL).
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.
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes.
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.
Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks
The natural world is full of complex systems characterized by intricate relations between their components: from social interactions between individuals in a social network to electrostatic interactions between atoms in a protein.
Intentional Choreography with Semi-Supervised Recurrent VAEs
We summarize the model and results of PirouNet, a semi-supervised recurrent variational autoencoder.
PirouNet: Creating Dance through Artist-Centric Deep Learning
Using Artificial Intelligence (AI) to create dance choreography with intention is still at an early stage.
