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Quantized Neural Networks for Radar Interference Mitigation., , , , and . CoRR, (2020)Manual versus Automated: The Challenging Routine of Infant Vocalisation Segmentation in Home Videos to Study Neuro(mal)development., , , , , , and . INTERSPEECH, page 2997-3001. ISCA, (2016)End-to-End Keyword Spotting Using Neural Architecture Search and Quantization., , and . ICASSP, page 3423-3427. IEEE, (2022)On Resource-Efficient Bayesian Network Classifiers and Deep Neural Networks., , , and . ICPR, page 10297-10304. IEEE, (2020)Paracyclophanes as model compounds for strongly interacting π-systems. Part 1. Pseudo-ortho-dihydroxy2.2paracyclophane, , , , , , and . Phys. Chem. Chem. Phys., 12 (32): 9339-9346 (2010)Paracyclophanes as model compounds for strongly interacting $\uppi$-systems. Part 2: mono-hydroxy2.2paracyclophane, , , , , and . Physical Chemistry Chemical Physics, 13 (23): 11076 (2011)Resource-Efficient Deep Neural Networks for Automotive Radar Interference Mitigation., , , , and . IEEE J. Sel. Top. Signal Process., 15 (4): 927-940 (2021)Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning., , , , , , , , and . ICTSS, volume 11812 of Lecture Notes in Computer Science, page 3-21. Springer, (2019)Differentiable TAN Structure Learning for Bayesian Network Classifiers., and . PGM, volume 138 of Proceedings of Machine Learning Research, page 389-400. PMLR, (2020)Paracyclophanes as Model Compounds for Strongly Interacting $\uppi$-Systems, Part 3: Influence of the Substitution Pattern on Photoabsorption Properties, , , , , , , , , and . The Journal of Physical Chemistry A, 115 (15): 3583--3591 (April 2011)