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Karlsruhe 2024 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 96: Data, AI, Computing 7 (uncertainties, likelihoods)

Donnerstag, 7. März 2024, 16:00–18:15, Geb. 30.33: MTI

16:00 T 96.1 Effects of adversarial attacks and defenses on generic neural network applications in high energy physics — •Timo Saala and Matthias Schott
16:15 T 96.2 Using Adversarial Attacks to Fool IceCube's Deep Neural Networks — •Oliver Janik, Philipp Soldin, and Christopher Wiebusch
16:30 T 96.3 Sharing AI-based Searches with Classifier Surrogates — •Sebastian Bieringer, Gregor Kasieczka, Jan Kieseler, and Mathias Trabs
16:45 T 96.4 Combining data with unknown correlations — •Lukas Koch
17:00 T 96.5 Binary Black Hole Parameter Estimation using a Conditioned Normalizing Flow — •Markus Bachlechner, Oliver Pooth, and Achim Stahl
17:15 T 96.6 Probabilistic Machine Learning for the XENONnT position reconstruction — •Sebastian Vetter
17:30 T 96.7 dilax: Differentiable Binned Likelihoods in JAXPeter Fackeldey, Benjamin Fischer, •Felix Zinn, and Martin Erdmann
  17:45 T 96.8 The contribution has been withdrawn (speaker takes the previous talk).
18:00 T 96.9 Refining Fast Simulations using Machine Learning TechniquesSamuel Bein, Patrick Connor, Sebastian Götschel, Daniel Ruprecht, Peter Schleper, •Lars Stietz, and Moritz Wolf
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