Summary of Topobenchmarkx: a Framework For Benchmarking Topological Deep Learning, by Lev Telyatnikov et al.
TopoBenchmarkX: A Framework for Benchmarking Topological Deep Learning
by Lev Telyatnikov, Guillermo Bernardez, Marco Montagna, Pavlo Vasylenko, Ghada Zamzmi, Mustafa Hajij, Michael T Schaub, Nina Miolane, Simone Scardapane, Theodore Papamarkou
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces TopoBenchmarkX, an open-source library that standardizes benchmarking and accelerates research in Topological Deep Learning (TDL). It maps the TDL pipeline into modular components, allowing flexibility and modification of various pipelines. The key feature is transformation and lifting between topological domains, enabling richer data representations and fine-grained analyses. Several TDL architectures are benchmarked for various tasks and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TopoBenchmarkX is a tool that helps scientists compare different ways of doing deep learning with special properties, like graphs or networks. It’s designed to make it easier to test and improve these methods by breaking them down into smaller parts. This library can also transform data from one type to another, allowing for more detailed analysis. The paper shows how this library works well for many different machine learning models and tasks. |
Keywords
» Artificial intelligence » Deep learning » Machine learning
