3 papers
math.NA2024
Learning-based approaches for reconstructions with inexact operators in nanoCT applications
Tom Lütjen, Fabian Schönfeld, Alice Oberacker +4
Imaging problems such as the one in nanoCT require the solution of an inverse problem, where it is often taken for granted that the forward operator, i.e., the underlying physical…
cs.CL2024
Combining Data Generation and Active Learning for Low-Resource Question Answering
Maximilian Kimmich, Andrea Bartezzaghi, Jasmina Bogojeska +2
Neural approaches have become very popular in Question Answering (QA), however, they require a large amount of annotated data. In this work, we propose a novel approach that combin…
cs.CV2024
Smooth Deep Saliency
Rudolf Herdt, Maximilian Schmidt, Daniel Otero Baguer +1
In this work, we investigate methods to reduce the noise in deep saliency maps coming from convolutional downsampling. Those methods make the investigated models more interpretable…