Results 31 to 40 of about 3,934 (193)
LogSE: An Uncertainty-Based Multi-Task Loss Function for Learning Two Regression Tasks [PDF]
Journal of Universal Computer Science, 2022 Multi-task learning (MTL) is a popular method in machine learning which utilizes related information of multi tasks to learn a task more efficiently and accurately.
Zeinab Ghasemi-Naraghi +2 more
doaj +3 more sources
Quantifying uncertainty of machine learning methods for loss given default
Frontiers in Applied Mathematics and Statistics, 2022 Machine learning has increasingly found its way into the credit risk literature. When applied to forecasting credit risk parameters, the approaches have been found to outperform standard statistical models. The quantification of prediction uncertainty is
Matthias Nagl +2 more
doaj +1 more source
Applied Sciences, 2021 Aleatoric and epistemic uncertainties can be represented probabilistically in mechanical systems. However, the distribution parameters of epistemic uncertainties are also uncertain due to sparsely available or inaccurate uncertainty information ...
Yufeng Lyu +4 more
doaj +1 more source
Aleatorism and Sporting Performance [PDF]
Physical Culture and Sport. Studies and Research, 2017 AbstractA sporting spectacle is an important cultural event, essentially influencing social and individual lives. In spite of this, there does not yet exist a monograph that analyzes, describes, and explains sporting performance from the point of view of aleatorism as part of the theory of sport and physical culture.
openaire +2 more sources
Rethinking Aleatoric and Epistemic Uncertainty
CoRRPublished at ICML ...
Bickford Smith, F +5 more
openaire +5 more sources
, 2021 Aleatoric Anthems and PalindromesAleatoric Anthems and Palindromes is a collection of music composed by Lars Bröndum 2020-2021. The music was performed by the Aleatoric Chamber Ensemble: My Eklund, Per Gärdin, Lisa Ullén, Lars Bröndum, Daniel Borgegård ...
Bröndum, Lars,
core
One step closer to unbiased aleatoric uncertainty estimation [PDF]
, 2023 Neural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making. In the deep learning field, the uncertainties are usually categorized into aleatoric (data) and epistemic (model ...
Megretski, Alexandre +6 more
core +1 more source
Tarantos by Leo Brouwer as an Example of Aleatoric Composition in Guitar Music
KonservatoryumThe 20th century marked a period of profound transformations in European classical music, leading to the emergence of various musical movements. Aleatoric music, characterized by the incorporation of chance elements into the composition or performance ...
Tuba Akkaya, Onur Kırım
doaj +1 more source
Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT–2 SAR Images and CNNs
Remote Sensing, 2022 With the increasing availability of SAR imagery in recent years, more research is being conducted using deep learning (DL) for the classification of ice and open water; however, ice and open water classification using conventional DL methods such as ...
Nastaran Saberi +2 more
doaj +1 more source
, 2023 Machine learning (ML) models can underperform on certain population groups due to choices made during model development and bias inherent in the data. We categorize sources of discrimination in the ML pipeline into two classes: aleatoric discrimination ...
He, Luxi
core +4 more sources