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Found 3 papers, 2 papers with code
Convolutional Neural Operators for robust and accurate learning of PDEs
Although very successfully used in conventional machine learning, convolution based neural network architectures -- believed to be inconsistent in function space -- have been largely ignored in the context of learning solution operators of PDEs.
Localized adversarial artifacts for compressed sensing MRI
As interest in deep neural networks (DNNs) for image reconstruction tasks grows, their reliability has been called into question (Antun et al., 2020; Gottschling et al., 2020).
ADef: an Iterative Algorithm to Construct Adversarial Deformations
While deep neural networks have proven to be a powerful tool for many recognition and classification tasks, their stability properties are still not well understood.
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