Results 31 to 40 of about 201,968 (266)
Journal of Modern Power Systems and Clean Energy, 2022 Modern power systems are incorporated with distributed energy sources to be environmental-friendly and cost-effective. However, due to the uncertainties of the system integrated with renewable energy sources, effective strategies need to be adopted to ...
Shiyao Zhang, James J.Q. Yu
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High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep Models
, 2015 Learning in deep models using Bayesian methods has generated significant attention recently. This is largely because of the feasibility of modern Bayesian methods to yield scalable learning and inference, while maintaining a measure of uncertainty in the
Carin, Lawrence +3 more
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IEEE Access, 2021 Deep learning has demonstrated high accuracy for 3D object shape error modeling necessary to estimate dimensional and geometric quality defects in multi-station assembly systems (MAS).
Sumit Sinha +2 more
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Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa
, 2018 This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted ...
Damianou, Andreas +3 more
core +1 more source
Early Detection of the Advanced Persistent Threat Attack Using Performance Analysis of Deep Learning
IEEE Access, 2020 One of the most common and critical destructive attacks on the victim system is the advanced persistent threat (APT)-attack. An APT attacker can achieve its hostile goal through obtaining information and gaining financial benefits from the infrastructure
Javad Hassannataj Joloudari +5 more
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Bayesian Deep Learning for Dark Energy
, 2020 In this work we discuss basic ideas on how to structure and study the Bayesian methods for standard models of dark energy and how to implement them in the architecture of deep learning processes.
openaire +4 more sources
Bayesian Deep Neural Network to Compensate for Current Transformer Saturation
IEEE Access, 2021 Current transformer saturation has a negative effect on the operation of IEDs, resulting in their malfunction. Here, we present a technique to compensate for saturated waveforms using Bayesian Deep Neural Network (BDNN) comprising Deep Neural Network ...
Sopheap Key +3 more
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Long‐Term Follow‐Up of Chemotherapy‐Associated Biological Aging in Women With Early Breast Cancer
Aging and Cancer, EarlyView.Women threated with adjuvant chemotherapy for early breast cancer have sustained long‐term increase in p16INK4a,, a robust marker of cell senescence, suggesting a chemotherapy‐associated age acceleration. p16INK4a as well as other biomarkers may identify patients at greatest risk for senescence‐related diseases of aging.
Hyman B. Muss +12 more
wiley +1 more source
Forecasting VIX using Bayesian deep learning
International Journal of Data Science and AnalyticsAbstract Recently, deep learning techniques are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. In this paper, we leverage probabilistic deep learning for inferring the volatility index VIX.
Hortúa, Héctor J. +1 more
openaire +2 more sources
Uncertainty Assessment-Based Active Learning for Reliable Fire Detection Systems
IEEE Access, 2022 Deep learning technologies, due to their advanced pattern extraction and recognition of high-dimensional data, have been widely adopted into multisensor-based fire detection systems.
Young-Jin Kim, Won-Tae Kim
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