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Generative Models

TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models
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AI Generated Machine Learning Generative Models 🏢 University of Cambridge
TabEBM: Class-specific EBMs boost tabular data augmentation, improving classification accuracy, especially on small datasets, by generating high-quality synthetic data.
Piecewise deterministic generative models
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Machine Learning Generative Models 🏢 École Polytechnique
Novel generative models based on piecewise deterministic Markov processes (PDMPs) are introduced, offering efficient training procedures and theoretical guarantees, surpassing diffusion-based models i…
Learning Distributions on Manifolds with Free-Form Flows
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Machine Learning Generative Models 🏢 Heidelberg University
Manifold Free-Form Flows (M-FFF) achieves fast and accurate generative modeling on Riemannian manifolds using a single function evaluation, outperforming prior methods.
Generative Fractional Diffusion Models
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Machine Learning Generative Models 🏢 Fraunhofer HHI
Generative Fractional Diffusion Models (GFDM) leverages fractional diffusion processes for superior image generation, enhancing diversity and quality while addressing existing diffusion model limitati…
Categorical Flow Matching on Statistical Manifolds
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AI Generated Machine Learning Generative Models 🏢 Peking University
Statistical Flow Matching (SFM) uses information geometry to create a new flow-matching framework for generating discrete data, achieving superior sampling quality and likelihood compared to existing …