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Found 7 papers, 6 papers with code
Continuous PDE Dynamics Forecasting with Implicit Neural Representations
Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations.
Diverse Weight Averaging for Out-of-Distribution Generalization
Standard neural networks struggle to generalize under distribution shifts in computer vision.
Generalizing to New Physical Systems via Context-Informed Dynamics Model
Data-driven approaches to modeling physical systems fail to generalize to unseen systems that share the same general dynamics with the learning domain, but correspond to different physical contexts.
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 domain adaptation with non-stochastic missing data
Moreover, we compare the target error of our Adaptation-imputation framework and the "ideal" target error of a UDA classifier without missing target components.
Unsupervised domain adaptation with imputation
Motivated by practical applications, we consider unsupervised domain adaptation for classification problems, in the presence of missing data in the target domain.
Benchmarking Regression Methods: A comparison with CGAN
Such a real world situation is best represented using an implicit model in which an extra noise vector, $z$ is included with $x$ as input.
