Sökning: "aleatoric"

Hittade 4 uppsatser innehållade ordet aleatoric.

1. 1. Uncertainty Estimation in Radiation Dose Prediction U-Net

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Frida Skarf; [2023]
   Nyckelord :Radiation dose prediction models; U-net; quantile regression; Monte Carlo Dropout; epistemic uncertainty estimation; aleatoric uncertainty estimation; Stråldospredicerande modeller; U-net; kvantilregression; Monte Carlo Dropout; epistemisk osäkerhetsskattning; aletorisk osäkerhetsskattning;

   Sammanfattning : The ability to quantify uncertainties associated with neural network predictions is crucial when they are relied upon in decision-making processes, especially in safety-critical applications like radiation therapy. In this paper, a single-model estimator of both epistemic and aleatoric uncertainties in a regression 3D U-net used for radiation dose prediction is presented. LÄS MER

3. 2. Uncertainty Estimation in Volumetric Image Segmentation

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Donggyun Park; [2023]
   Nyckelord :Uncertainty Estimation; Uncertainty Quantification UQ ; Volumetric Image Segmentation; 3D U-Net; test-time data augmentation; Deep ensemble;

   Sammanfattning : The performance of deep neural networks and estimations of their robustness has been rapidly developed. In contrast, despite the broad usage of deep convolutional neural networks (CNNs)[1] for medical image segmentation, research on their uncertainty estimations is being far less conducted. LÄS MER

4. 3. Real-time Uncertainty Estimation for Semantic Segmentation : Improving Uncertainty Estimates with Temperature Scaling and Predicted Dirichlet Distributions

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Lukas Grannas; [2020]
   Nyckelord :;

   Sammanfattning : This degree project examined different aspects of real-time uncertainty estimation for semantic segmentation deep learning networks in an autonomous driving setting. Two main tracks were taken. LÄS MER

5. 4. Probabilistic Regression using Conditional Generative Adversarial Networks

   Master-uppsats, Linköpings universitet/Statistik och maskininlärning

   Författare :Joel Oskarsson; [2020]
   Nyckelord :machine learning; ml; regression; probabilistic; distribution; cgan; gan; conditional gan; adversarial networks; neural network; deep learning; f-gan; f-cgan; f-divergence; adversarial training; bimodal; heteroskedastic; mmd; maximum mean discrepancy; gmmn; generative moment matching network; conditional gmmn; ipm; kde; cgan evaluation; cgan regression; gan regression; cgan-regression; regression using gan; deep; nn; implicit; generative; conditional; model; complex noise; aleatoric; uncertainty; dctd; mdn; heteroskedastic regression; gp;

   Sammanfattning : Regression is a central problem in statistics and machine learning with applications everywhere in science and technology. In probabilistic regression the relationship between a set of features and a real-valued target variable is modelled as a conditional probability distribution. LÄS MER