Summary of Conformally Natural Families Of Probability Distributions on Hyperbolic Disc with a View on Geometric Deep Learning, by Vladimir Jacimovic and Marijan Markovic
Conformally Natural Families of Probability Distributions on Hyperbolic Disc with a View on Geometric Deep Learning
by Vladimir Jacimovic, Marijan Markovic
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Complex Variables (math.CV)
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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 presents a new family of probability distributions on the hyperbolic disc, which exhibits group-invariance under conformal mappings. This property makes it suitable for encoding uncertainties in hyperbolic data and has potential applications in Geometric Deep Learning and bioinformatics. The proposed model can be used to represent uncertainties in geometric deep learning models and may lead to improved performance in tasks such as graph classification and clustering. Additionally, the analogy with hyperbolic coherent states in quantum physics highlights the connection between geometry and uncertainty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new type of math problem that helps us understand how to deal with uncertain data when we’re working on shapes and patterns. The idea is to use special transformations called conformal mappings to make sure our calculations are correct, even if the data is messy or incomplete. This could be useful for things like classifying graphs or finding patterns in biology. |
Keywords
» Artificial intelligence » Classification » Clustering » Deep learning » Probability
