3 papers
cs.LG2026
Parallel Noising in Neural Markov Logic Networks
Peter Jung, Giuseppe Marra, Ondrej Kuzelka
Neural Markov Logic Networks (NMLNs) are a flexible neurosymbolic relational model. Previous work has shown that, although NMLNs achieve strong performance as generative models for…
physics.optics2026
Physics-constrained neural networks for surrogate modeling of lossless periodic structures
Eric Prehn, Peter Jung
We introduce a physics-constrained neural network for the rapid prediction of rigorous coupled-wave analysis outputs in the form of Jones matrices. Starting from energy conservatio…
cs.IT2024
Non-negative Sparse Recovery at Minimal Sampling Rate
Hendrik Bernd Zarucha, Peter Jung
It is known that sparse recovery is possible if the number of measurements is in the order of the sparsity, but the corresponding decoders either lack polynomial decoding time or r…