Temporal Evaluation of Probability Calibration with Experimental Errors
Paper in proceeding, 2025
The quantification of uncertainties associated with neural network predictions can facilitate optimal decision-making and accelerate workflows where time and resource efficiency are essential.
Author
Hannah Rosa Friesacher
AstraZeneca AB
KU Leuven
Emma Svensson
Johannes Kepler University of Linz (JKU)
AstraZeneca AB
Ádám Arany
KU Leuven
Lewis H. Mervin
AstraZeneca AB
Ola Engkvist
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
AstraZeneca AB
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
03029743 (ISSN) 16113349 (eISSN)
Vol. 14894 LNCS 13-209783031723803 (ISBN)
1st International Workshop on AI in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024
Lugano, Switzerland,
Lugano, Switzerland,
Subject Categories
Bioinformatics (Computational Biology)
DOI
10.1007/978-3-031-72381-0_2