Results 21 to 30 of about 173,823 (173)
Сучасні інформаційні системи, 2019 This paper contains formal problem definition of predicting unfavorable airborne events during flight. Restrictions and assumptions are put into the prognosis method of unfavorable airborne events during flight.
Evhenii Gryshmanov +2 more
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Hyperparameter-free losses for model-based monocular reconstruction [PDF]
, 2019 This work proposes novel hyperparameter-free losses for single view 3D reconstruction with morphable models (3DMM). We dispense with the hyperparameters used in other works by exploiting geometry, so that the shape of the object and the camera pose are ...
Batard, Thomas +3 more
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Sensors, 2023 Semantic segmentation with deep learning networks has become an important approach to the extraction of objects from very high-resolution remote sensing images.
Jia Song, A-Xing Zhu, Yunqiang Zhu
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Hyperparameter Optimization for AST Differencing
IEEE Transactions on Software Engineering, 2023 Computing the differences between two versions of the same program is an essential task for software development and software evolution research. AST differencing is the most advanced way of doing so, and an active research area. Yet, AST differencing algorithms rely on configuration parameters that may have a strong impact on their effectiveness.
Matias Martinez +2 more
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Interpolation Models with Multiple Hyperparameters [PDF]
Statistics and Computing, 1996 A traditional interpolation model is characterized by the choice of regularizer applied to the interpolant, and the choice of noise model. Typically, the regularizer has a single regularization constant α, and the noise model has a single parameter β.
DAVID J. C. MACKAY, RYO TAKEUCHI
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A Joint-Parameter Estimation and Bayesian Reconstruction Approach to Low-Dose CT
Sensors, 2023 Most penalized maximum likelihood methods for tomographic image reconstruction based on Bayes’ law include a freely adjustable hyperparameter to balance the data fidelity term and the prior/penalty term for a specific noise–resolution tradeoff.
Yongfeng Gao +7 more
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PyHopper -- Hyperparameter optimization
, 2022 Hyperparameter tuning is a fundamental aspect of machine learning research. Setting up the infrastructure for systematic optimization of hyperparameters can take a significant amount of time. Here, we present PyHopper, a black-box optimization platform designed to streamline the hyperparameter tuning workflow of machine learning researchers. PyHopper's
Lechner, Mathias +4 more
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Applied SciencesSymmetric data play an effective role in the risk assessment process, and, therefore, integrating symmetrical information using Failure Mode and Effects Analysis (FMEA) is essential in implementing projects with big data. This proactive approach helps to
Naeim Rezaeian +5 more
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Brain Tumor Detection and Classification Using an Optimized Convolutional Neural Network
DiagnosticsBrain tumors are a leading cause of death globally, with numerous types varying in malignancy, and only 12% of adults diagnosed with brain cancer survive beyond five years.
Muhammad Aamir +6 more
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Hyperparameter-Optimization-Inspired Long Short-Term Memory Network for Air Quality Grade Prediction
Information, 2023 In the world, with the continuous development of modern society and the acceleration of urbanization, the problem of air pollution is becoming increasingly salient. Methods for predicting the air quality grade and determining the necessary governance are
Dushi Wen +5 more
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