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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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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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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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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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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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SN Applied Sciences, 2022 The deep convolutional neural network (ConvNet) achieves significant segmentation performance on medical images of various modalities. However, the isolated errors in a large testing set with various tumor conditions are not acceptable in clinical ...
Ge Zhang, Hao Dang, Yulong Xu
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IEEE Access, 2020 In the current scenario, it is significant to design active learning paradigms for analyzing human activities using Wearable Internet of Things (W-IoT) sensors for health parameter analysis.
Zhiqing Zhou, Heng Yu, Hesheng Shi
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Cold Posteriors and Aleatoric Uncertainty
CoRR, 2020 5 pages, 3 ...
Ben Adlam, Jasper Snoek, Samuel L. Smith
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Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation [PDF]
, 2021 Bayesian optimisation is a sample-efficient search methodology that holds great promise for accelerating drug and materials discovery programs.
Lalchand, Vidhi R. +4 more
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