CountsDiff: A Diffusion Model on the Natural Numbers for Generation and Imputation of Count-Based Data
arXiv:2604.03779v2 Announce Type: replace Abstract: Diffusion models have excelled at generative tasks for both continuous and token-based domains, but their application to discrete ordinal data remains underdeveloped. We present CountsDiff, a diffusion framework designed to model distributions on the natural numbers. CountsDiff extends the Blackout diffusion framework by simplifying its formulation through a direct parameterization in terms of a survival probability schedule and an explicit loss weighting. This introduces flexibility through design parameters with direct analogues in existing
The continuous development in AI aims to expand the applicability of diffusion models to a broader range of data types, addressing the current limitations in handling discrete ordinal datasets.
This development could enhance the accuracy and utility of generative AI in fields relying on count-based data, potentially improving predictions and simulations in various industries.
Diffusion models, traditionally strong in continuous and token-based domains, are now being refined to effectively model discrete ordinal data, opening up new application areas.
- · AI researchers
- · Data scientists
- · Industries using count-based data (e.g., healthcare, finance)
- · Traditional statistical modeling methods
Improved generative capabilities for discrete, count-based data will emerge across various AI applications.
New AI products and services leveraging these enhanced models could be developed, particularly in areas like bioinformatics or economic forecasting.
The broader adoption of such models might lead to a re-evaluation of data collection and imputation strategies in fields previously underserved by current generative AI techniques.
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Read at arXiv cs.LG