An adversarially trained autoencoder learns a convex latent space to enable rapid approximate projections that enforce nonconvex constraints in optimization and reinforcement learning.
Geometric autoencoders–what you see is what you decode.arXiv preprint arXiv:2306.17638
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GABI learns geometry-conditioned latent priors from multi-geometry physical response datasets for use in Bayesian inversion, yielding geometry-adapted posteriors via ABC sampling.
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Improving Feasibility via Fast Autoencoder-Based Projections
An adversarially trained autoencoder learns a convex latent space to enable rapid approximate projections that enforce nonconvex constraints in optimization and reinforcement learning.
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Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later
GABI learns geometry-conditioned latent priors from multi-geometry physical response datasets for use in Bayesian inversion, yielding geometry-adapted posteriors via ABC sampling.