Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel
von Fabian Altekrüger, Paul Hagemann, Johannes Hertrich, Robert Beinert, Jannis Chemseddine, Gabriele Steidl
Jahr:
2024
Abstrakt:
We propose conditional flows of the maximum mean discrepancy (MMD) with the negative distance kernel for posterior sampling and conditional generative modelling. This MMD, which is also known as energy distance, has several advantageous properties like efficient computation via slicing and sorting.
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Brief introduction of the dida co-author(s) and relevance for dida's ML developments.
Fabian Altekrüger (PhD)
During his mathematics studies at TU Berlin, Fabian focused on functional analysis. In his subsequent doctoral research, he worked on the regularization and solution of Bayesian inverse problems in mathematical image processing, combining mathematical methods with neural networks. In this context, Fabian developed and applied conditional generative models, always with an emphasis on the stability and robustness of the methods. At dida, he contributes his skills as a machine learning scientist.