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Found 5 papers, 5 papers with code
A Structured Matrix Method for Nonequispaced Neural Operators
The computational efficiency of many neural operators, widely used for learning solutions of PDEs, relies on the fast Fourier transform (FFT) for performing spectral computations.
Mapping conditional distributions for domain adaptation under generalized target shift
We consider the problem of unsupervised domain adaptation (UDA) between a source and a target domain under conditional and label shift a. k. a Generalized Target Shift (GeTarS).
Unsupervised Adversarial Image Inpainting
This allows us sampling from the latent component in order to generate a distribution of images associated to an observation.
Unsupervised Adversarial Image Reconstruction
We address the problem of recovering an underlying signal from lossy, inaccurate observations in an unsupervised setting.
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
We consider the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes.
