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Found 4 papers, 1 papers with code
Generative Modelling with Tensor Train approximations of Hamilton--Jacobi--Bellman equations
Sampling from probability densities is a common challenge in fields such as Uncertainty Quantification (UQ) and Generative Modelling (GM).
Happy People -- Image Synthesis as Black-Box Optimization Problem in the Discrete Latent Space of Deep Generative Models
In recent years, optimization in the learned latent space of deep generative models has been successfully applied to black-box optimization problems such as drug design, image generation or neural architecture search.
Ensemble-based gradient inference for particle methods in optimization and sampling
We propose an approach based on function evaluations and Bayesian inference to extract higher-order differential information of objective functions {from a given ensemble of particles}.
Consistency analysis of bilevel data-driven learning in inverse problems
One fundamental problem when solving inverse problems is how to find regularization parameters.
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