Karlsruhe 2024 –
wissenschaftliches Programm
T 96: Data, AI, Computing 7 (uncertainties, likelihoods)
Donnerstag, 7. März 2024, 16:00–18:15, Geb. 30.33: MTI
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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
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16:15 |
T 96.2 |
Using Adversarial Attacks to Fool IceCube's Deep Neural Networks — •Oliver Janik, Philipp Soldin, and Christopher Wiebusch
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16:30 |
T 96.3 |
Sharing AI-based Searches with Classifier Surrogates — •Sebastian Bieringer, Gregor Kasieczka, Jan Kieseler, and Mathias Trabs
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16:45 |
T 96.4 |
Combining data with unknown correlations — •Lukas Koch
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17:00 |
T 96.5 |
Binary Black Hole Parameter Estimation using a Conditioned Normalizing Flow — •Markus Bachlechner, Oliver Pooth, and Achim Stahl
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17:15 |
T 96.6 |
Probabilistic Machine Learning for the XENONnT position reconstruction — •Sebastian Vetter
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17:30 |
T 96.7 |
dilax: Differentiable Binned Likelihoods in JAX — Peter Fackeldey, Benjamin Fischer, •Felix Zinn, and Martin Erdmann
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17:45 |
T 96.8 |
The contribution has been withdrawn (speaker takes the previous talk).
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18:00 |
T 96.9 |
Refining Fast Simulations using Machine Learning Techniques — Samuel Bein, Patrick Connor, Sebastian Götschel, Daniel Ruprecht, Peter Schleper, •Lars Stietz, and Moritz Wolf
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