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Manifold Diffusion Fields

This paper, accepted for NeurIPS 2023’s Diffusion Models workshop, discusses the challenges in adapting score-based generative models to various data domains and proposes a solution using a functional view of data for a unified representation and reformulated score function.

 Manifold Diffusion Fields

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Transformative AI Technology: At the forefront of innovation, score-based models are revolutionizing the way we handle data in images, text, and even complex structures like molecules. However, these models require specialized design for each data type, which can be a limitation.

Introducing Manifold Diffusion Fields: Our latest paper, presented at the Diffusion Models workshop at NeurIPS 2023, introduces a groundbreaking approach. By viewing data from a functional perspective, we can unify different data types into a common format, simplifying the AI model design process.

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Vladimir Dyachkov, Ph.D
Editor-in-Chief itinai.com

I believe that AI is only as powerful as the human insight guiding it.

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