Building Normalizing Flows with Stochastic Interpolants
Explore advanced techniques for constructing normalizing flows using stochastic interpolants, enhancing generative modeling capabilities.
Reflected Diffusion Models
Explore innovative techniques for enhancing diffusion models, improving sample quality and efficiency in generative AI applications.
Action Matching: Learning Stochastic Dynamics from Samples
Learn stochastic dynamics from samples using action matching techniques. Explore innovative approaches to understanding and modeling complex systems through data-driven methods.
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
Explore discrete diffusion modeling techniques for estimating data distribution ratios, enhancing generative AI capabilities and understanding.
Tractable Probabilistic Circuits - Seminar Series
Explore probabilistic circuits as advanced neural networks for tractable inference, outperforming VAEs and flow-based models. Learn about their applications in machine learning and neural compression.
Diffusion Models
Explore advanced concepts in diffusion models, including theory, applications, and recent developments in probabilistic AI.
Navigating, Restructuring and Reshaping Learned Latent Spaces
Explore advanced techniques for manipulating learned latent spaces in machine learning, focusing on navigation, restructuring, and reshaping methods for improved model performance and efficiency.