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Found 3 papers, 2 papers with code
Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks
While generated samples often are indistinguishable from real data, mode-collapse may occur and there is no guarantee that they will follow the true data distribution.
Physics-informed neural networks with unknown measurement noise
Physics-informed neural networks (PINNs) constitute a flexible approach to both finding solutions and identifying parameters of partial differential equations.
Incorporating Sum Constraints into Multitask Gaussian Processes
Machine learning models can be improved by adapting them to respect existing background knowledge.
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