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Uncertainty

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Optimistic reinforcement learning by forward Kullback-Leibler divergence optimization.

Neural networks : the official journal of the International Neural Network Society
This paper addresses a new interpretation of the traditional optimization method in reinforcement learning (RL) as optimization problems using reverse Kullback-Leibler (KL) divergence, and derives a new optimization method using forward KL divergence...

Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning.

Medical image analysis
Due to the difficulty in accessing a large amount of labeled data, semi-supervised learning is becoming an attractive solution in medical image segmentation. To make use of unlabeled data, current popular semi-supervised methods (e.g., temporal ensem...

Can uncertainty estimation predict segmentation performance in ultrasound bone imaging?

International journal of computer assisted radiology and surgery
PURPOSE: Segmenting bone surfaces in ultrasound (US) is a fundamental step in US-based computer-assisted orthopaedic surgeries. Neural network-based segmentation techniques are a natural choice for this, given promising results in related tasks. Howe...

Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images.

IEEE transactions on medical imaging
Despite the promise of Convolutional neural network (CNN) based classification models for histopathological images, it is infeasible to quantify its uncertainties. Moreover, CNNs may suffer from overfitting when the data is biased. We show that Bayes...

A Novel Prescribed-Performance-Tracking Control System with Finite-Time Convergence Stability for Uncertain Robotic Manipulators.

Sensors (Basel, Switzerland)
Through this article, we present an advanced prescribed performance-tracking control system with finite-time convergence stability for uncertain robotic manipulators. It is therefore necessary to define a suitable performance function and error trans...

Bayesian deep learning-based H-MRS of the brain: Metabolite quantification with uncertainty estimation using Monte Carlo dropout.

Magnetic resonance in medicine
PURPOSE: To develop a Bayesian convolutional neural network (BCNN) with Monte Carlo dropout sampling for metabolite quantification with simultaneous uncertainty estimation in deep learning-based proton MRS of the brain.

Research and Forecast Analysis of Financial Stability for Policy Uncertainty.

Computational intelligence and neuroscience
The instability of financial market will have a great impact on money, bonds, and stocks and affect the economic development of society and people's lives. Therefore, it is very necessary for us to study and predict the financial stability. According...

Method to Minimize the Errors of AI: Quantifying and Exploiting Uncertainty of Deep Learning in Brain Tumor Segmentation.

Sensors (Basel, Switzerland)
Despite the unprecedented success of deep learning in various fields, it has been recognized that clinical diagnosis requires extra caution when applying recent deep learning techniques because false prediction can result in severe consequences. In t...

Quantifying uncertainty in machine learning classifiers for medical imaging.

International journal of computer assisted radiology and surgery
PURPOSE: Machine learning (ML) models in medical imaging (MI) can be of great value in computer aided diagnostic systems, but little attention is given to the confidence (alternatively, uncertainty) of such models, which may have significant clinical...

Challenges and Opportunities for Bayesian Statistics in Proteomics.

Journal of proteome research
Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine learning, are routinely applied. For a quantity of ...