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pro vyhledávání: '"Widmann, David"'
Publikováno v:
International Conference on Learning Representations (2021)
Most supervised machine learning tasks are subject to irreducible prediction errors. Probabilistic predictive models address this limitation by providing probability distributions that represent a belief over plausible targets, rather than point esti
Externí odkaz:
http://arxiv.org/abs/2210.13355
Autor:
Widmann, David, Rackauckas, Chris
Traditional solvers for delay differential equations (DDEs) are designed around only a single method and do not effectively use the infrastructure of their more-developed ordinary differential equation (ODE) counterparts. In this work we present Dela
Externí odkaz:
http://arxiv.org/abs/2208.12879
Autor:
Widmann, David
Predicting unknown and unobserved events is a common task in many domains. Mathematically, the uncertainties arising in such prediction tasks can be described by probabilistic predictive models. Ideally, the model estimates of these uncertainties all
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429418
Publikováno v:
Advances in Neural Information Processing Systems 32 (NeurIPS 2019)
In safety-critical applications a probabilistic model is usually required to be calibrated, i.e., to capture the uncertainty of its predictions accurately. In multi-class classification, calibration of the most confident predictions only is often not
Externí odkaz:
http://arxiv.org/abs/1910.11385
Autor:
Vaicenavicius, Juozas, Widmann, David, Andersson, Carl, Lindsten, Fredrik, Roll, Jacob, Schön, Thomas B.
Probabilistic classifiers output a probability distribution on target classes rather than just a class prediction. Besides providing a clear separation of prediction and decision making, the main advantage of probabilistic models is their ability to
Externí odkaz:
http://arxiv.org/abs/1902.06977
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