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Fisher Flow Matching for Discrete Data by aref.shahbakhsh1998 is a document available to read on EtoBox.

The document introduces F ISHER -F LOW, a novel flow-matching model for generative modeling over discrete data, leveraging the Fisher-Rao metric to treat categorical distributions as points on a statistical manifold. This approach allows for continuous reparameterization of discrete data, enabling improved training dynamics and performance in tasks such as DNA sequence design. Empirical evaluations demonstrate that F ISHER -F LOW outperforms existing diffusion and flow-matching models on various benchmarks.

Author
aref.shahbakhsh1998
Language
EN