Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

no code implementations • 10 May 2022 • Julian Wörmann, Daniel Bogdoll, Christian Brunner, Etienne Bührle, Han Chen, Evaristus Fuh Chuo, Kostadin Cvejoski, Ludger van Elst, Philip Gottschall, Stefan Griesche, Christian Hellert, Christian Hesels, Sebastian Houben, Tim Joseph, Niklas Keil, Johann Kelsch, Mert Keser, Hendrik Königshof, Erwin Kraft, Leonie Kreuser, Kevin Krone, Tobias Latka, Denny Mattern, Stefan Matthes, Franz Motzkus, Mohsin Munir, Moritz Nekolla, Adrian Paschke, Stefan Pilar von Pilchau, Maximilian Alexander Pintz, Tianming Qiu, Faraz Qureishi, Syed Tahseen Raza Rizvi, Jörg Reichardt, Laura von Rueden, Alexander Sagel, Diogo Sasdelli, Tobias Scholl, Gerhard Schunk, Gesina Schwalbe, Hao Shen, Youssef Shoeb, Hendrik Stapelbroek, Vera Stehr, Gurucharan Srinivas, Anh Tuan Tran, Abhishek Vivekanandan, Ya Wang, Florian Wasserrab, Tino Werner, Christian Wirth, Stefan Zwicklbauer

The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models.

Autonomous Driving BIG-bench Machine Learning

Leading-order QED radiative corrections to timelike Compton scattering on the proton

no code implementations • 17 Dec 2020 • Matthias Heller, Niklas Keil, Marc Vanderhaeghen

On the level of cross sections we find the QED radiative corrections to be in the $5 -10\%$ range in the low-energy regime and around $20\%$ in the high-energy regime.

High Energy Physics - Phenomenology Nuclear Theory