MIXED PROBABILITY MODELS FOR ALEATORIC UNCERTAINTY ESTIMATION IN THE CONTEXT OF DENSE STEREO MATCHING [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021 The ability to identify erroneous depth estimates is of fundamental interest. Information regarding the aleatoric uncertainty of depth estimates can be, for example, used to support the process of depth reconstruction itself.
Z. Zhong, M. Mehltretter
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Bayesian losses for homoscedastic aleatoric uncertainty modeling in pollen image detection [PDF]
Научно-технический вестник информационных технологий, механики и оптики, 2021 The paper investigates the homoscedastic aleatoric uncertainty modeling for the detection of pollen in images. The new uncertainty modeling loss functions are presented, which are based on the focal and smooth L1 losses.
Natalia E. Khanzhina
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Forecasting the Disturbance Storm Time Index with Bayesian Deep Learning
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 The disturbance storm time (Dst) index is an important and useful measurement in space weather research. It has been used to characterize the size and intensity of a geomagnetic storm.
Yasser Abduallah +5 more
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JOINT ESTIMATION OF DEPTH AND ITS UNCERTAINTY FROM STEREO IMAGES USING BAYESIAN DEEP LEARNING [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 The necessity to identify errors in the context of image-based 3D reconstruction has motivated the development of various methods for the estimation of uncertainty associated with depth estimates in recent years.
M. Mehltretter
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Intrinsic randomness in epidemic modelling beyond statistical uncertainty
Communications Physics, 2023 Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters). The majority of frameworks assessing infectious disease risk consider only epistemic uncertainty.
Matthew J. Penn +9 more
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Cohesion: A Measure of Organisation and Epistemic Uncertainty of Incoherent Ensembles
Entropy, 2023 This paper offers a measure of how organised a system is, as defined by self-consistency. Complex dynamics such as tipping points and feedback loops can cause systems with identical initial parameters to vary greatly by their final state.
Timothy Davey
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Roland Barthes' "Text" and aleatoric music: Is "The birth of the reader" the birth of the listener? [PDF]
Muzikologija, 2002 The history of Western classical music and the development of its notational system show that composers have tried to control more and more aspects of their compositions as precisely as possible.
Jeongwon Joe, Song Hoo S.
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Fairness through Aleatoric Uncertainty
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023 We propose a simple yet effective solution to tackle the often-competing goals of fairness and utility in classification tasks. While fairness ensures that the model's predictions are unbiased and do not discriminate against any particular group or individual, utility focuses on maximizing the model's predictive performance.
Anique Tahir, Lu Cheng, Huan Liu
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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
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Looking at the posterior: accuracy and uncertainty of neural-network predictions
Machine Learning: Science and Technology, 2023 Bayesian inference can quantify uncertainty in the predictions of neural networks using posterior distributions for model parameters and network output.
Hampus Linander +3 more
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