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cs.LG2025
Reward driven discovery of the optimal microstructure representations with invariant variational autoencoders
Boris N. Slautin, Kamyar Barakati, Hiroshi Funakubo +4
Microscopy techniques generate vast amounts of complex image data that in principle can be used to discover simpler, interpretable, and parsimonious forms to reveal the underlying…
cs.LG2025
Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model
Boris N. Slautin, Utkarsh Pratiush, Doru C. Lupascu +2
We introduce a Deep Kernel Learning Variational Autoencoder (VAE-DKL) framework that integrates the generative power of a Variational Autoencoder (VAE) with the predictive nature o…