Summary of Generative Assignment Flows For Representing and Learning Joint Distributions Of Discrete Data, by Bastian Boll et al.
Generative Assignment Flows for Representing and Learning Joint Distributions of Discrete Data
by Bastian Boll, Daniel Gonzalez-Alvarado, Stefania Petra, Christoph Schnörr
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 The proposed generative model represents joint probability distributions of multiple discrete random variables using measure transport by randomized assignment flows on a statistical submanifold of factorizing distributions. This enables efficient representation, sampling, and likelihood assessment for any target distribution, including unseen data points. The model’s complexity depends on the parametrization of the affinity function, which can be trained simulation-free via conditional Riemannian flow matching using geodesics on an assignment manifold. Experiments demonstrate scalability to large-scale problems in structured image labeling tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to represent and work with complex probability distributions that involve many random variables. It uses a special kind of flow to efficiently generate samples from these distributions, which is important for machine learning models that rely on them. The model can be trained without needing to simulate or predict the entire distribution, making it useful for large-scale problems in image labeling tasks and potentially other areas. |
Keywords
» Artificial intelligence » Generative model » Likelihood » Machine learning » Probability
