Results 31 to 40 of about 190,737 (267)
Current Directions in Biomedical Engineering, 2021 Intraoperative neurophysiological monitoring (IONM) is an essential tool during numerous surgical interventions to assess and monitor the functional integrity of neural structures at risk.
Kortus Tobias +3 more
doaj +1 more source
Bayesian Active Learning for Received Signal Strength-Based Visible Light Positioning
IEEE Photonics Journal, 2022 Visible Light Positioning (VLP) is a promising indoor localization technology for providing highly accurate positioning. In this work, a VLP implementation is employed to estimate the position of a vehicle in a room using the Received Signal Strength ...
Federico Garbuglia +5 more
doaj +1 more source
Bayesian active learning with basis functions [PDF]
2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2011 A common technique for dealing with the curse of dimensionality in approximate dynamic programming is to use a parametric value function approximation, where the value of being in a state is assumed to be a linear combination of basis functions. Even with this simplification, we face the exploration/exploitation dilemma: an inaccurate approximation may
Ilya O. Ryzhov, Warren B. Powell
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Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks [PDF]
, 2017 In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized Cognitive Radio Network (CRN) accessing the frequency band of a Primary User (PU) in an underlay cognitive scenario with a designed PU ...
Chatzinotas, Symeon +2 more
core +2 more sources
Probe microscopy is all you need
Machine Learning: Science and Technology, 2023 We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods.
Sergei V Kalinin +5 more
doaj +1 more source
Personalizing gesture recognition using hierarchical bayesian neural networks [PDF]
, 2017 Building robust classifiers trained on data susceptible to group or subject-specific variations is a challenging pattern recognition problem. We develop hierarchical Bayesian neural networks to capture subject-specific variations and share statistical ...
Betke, Margrit +4 more
core +1 more source
Deep Bayesian Active Semi-Supervised Learning [PDF]
2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018 In many applications the process of generating label information is expensive and time consuming. We present a new method that combines active and semi-supervised deep learning to achieve high generalization performance from a deep convolutional neural network with as few known labels as possible.
Rottmann, Matthias +2 more
openaire +2 more sources
The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes [PDF]
, 2014 Dopamine plays a key role in learning; however, its exact function in decision making and choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) inference wherein dopamine encodes the precision of beliefs about optimal ...
Dolan, Ray +4 more
core +2 more sources
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network [PDF]
, 2018 Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity.
B Ginneken van +6 more
core +2 more sources
Bayesian Phase Estimation via Active Learning
, 2021 Bayesian estimation approaches, which are capable of combining the information of experimental data from different likelihood functions to achieve high precisions, have been widely used in phase estimation via introducing a controllable auxiliary phase.
Qiu, Yuxiang +3 more
openaire +2 more sources