Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels
von Fabian Altekrüger, Johannes Hertrich, Gabriele Steidl
Jahr:
2023
Abstrakt:
Wasserstein gradient flows of maximum mean discrepancy (MMD) functionals with non-smooth Riesz kernels show a rich structure as singular measures can become absolutely continuous ones and conversely. In this paper we contribute to the understanding of such flows.
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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.