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Posterior-Variance-Based Error Quantification for Inverse Problems in Imaging
The proposed method employs estimates of the posterior variance together with techniques from conformal prediction in order to obtain coverage guarantees for the error bounds, without making any assumption on the underlying data distribution.
Unsupervised energy disaggregation via convolutional sparse coding
In this work, a method for unsupervised energy disaggregation in private households equipped with smart meters is proposed.
A Note on the Regularity of Images Generated by Convolutional Neural Networks
The regularity of images generated by convolutional neural networks, such as the U-net, generative networks, or the deep image prior, is analyzed.
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