Results 11 to 20 of about 4,779,838 (300)
DisSAGD: A Distributed Parameter Update Scheme Based on Variance Reduction [PDF]
Sensors, 2021 Machine learning models often converge slowly and are unstable due to the significant variance of random data when using a sample estimate gradient in SGD.
Haijie Pan, Lirong Zheng
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Fast PET reconstruction with variance reduction and prior-aware preconditioning [PDF]
Frontiers in Nuclear MedicineWe investigated subset-based optimization methods for positron emission tomography (PET) image reconstruction incorporating a regularizing prior. PET reconstruction methods that use a prior, such as the relative difference prior (RDP), are of particular ...
Matthias J. Ehrhardt +2 more
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Accelerated Stochastic Variance Reduction Gradient Algorithms for Robust Subspace Clustering [PDF]
SensorsRobust face clustering enjoys a wide range of applications for gate passes, surveillance systems and security analysis in embedded sensors. Nevertheless, existing algorithms have limitations in finding accurate clusters when data contain noise (e.g ...
Hongying Liu +5 more
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Optimization of variance reduction techniques used in EGSnrc Monte Carlo Codes [PDF]
Journal of Medical Physics, 2018 Monte Carlo (MC) simulations are often used in calculations of radiation transport to enable accurate prediction of radiation-dose, even though the computation is relatively time-consuming.
Sangeetha Shanmugasundaram +1 more
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TOWARDS ZERO-VARIANCE SCHEMES FOR KINETIC MONTE-CARLO SIMULATIONS [PDF]
EPJ Web of Conferences, 2021 The solution of the time-dependent transport problem for neutrons and precursors in a nuclear reactor is hard to treat in a naive Monte-Carlo framework because of the largely different time scales associated to the prompt-fission chains and to the decay ...
Mancusi Davide, Zoia Andrea
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, 2017 Increased computer speed and memory have encouraged simulation analysts to develop ever more realistic stochastic models. Despite these advancements in computing hardware, the most significant gains in the speed of stochastic simulation are still the result of methodological advances and clever algorithmic design.
Botev, Z, Ridder, A
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ROBUST SIMULATION METHOD OF COMPLEX TECHNICAL TRANSPORT SYSTEMS [PDF]
Transport Problems, 2021 In the optimization of technical systems focused on a specific functional purpose (reliability, safety, and availability) with the use of simulation methods, an important parameter is the digital simulation time of the research subject.
Janusz SZPYTKO, Yorlandys SALGADO DUARTE
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Pilot development: an empirical mixed-method analysis [PDF]
Journal of Defense Analytics and Logistics, 2022 Purpose – Pilot upgrade training is critical to aircraft and passenger safety. This study aims to identify variances in the US Air Force C-130J pilot upgrade training based on geographic location and provide a model to enhance policy that will impact ...
Jonathan Slottje +3 more
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ANALYSIS OF DOSE RATES AROUND THE SLOVENIAN SILO-TYPE LILW REPOSITORY USING ADVANTG [PDF]
EPJ Web of Conferences, 2021 The ADVANTG code was used to analyze dose rates from the proposed Slovenian silo-type low and intermediate level waste (LILW) repository. Detailed calculations of dose rates are challenging as gamma-sources are located in thick concrete containers and ...
Kotnik Domen +3 more
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A Flexible Stochastic Multi-Agent ADMM Method for Large-Scale Distributed Optimization
IEEE Access, 2022 While applying stochastic alternating direction method of multiplier (ADMM) methods has become enormously potential in distributed applications, improving the algorithmic flexibility can bring huge benefits.
Lin Wu, Yongbin Wang, Tuo Shi
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