Results 21 to 30 of about 62,767 (259)
This file contains hyperparameter tuning experiments.
Yiran Chen, Hai Li, Huanrui Yang
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Deep Learning in Forest Structural Parameter Estimation Using Airborne LiDAR Data
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Accurately estimating and mapping forest structural parameters are essential for monitoring forest resources and understanding ecological processes. The novel deep learning algorithm has the potential to be a promising approach to improve the estimation ...
Hao Liu +7 more
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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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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: A Spectral Approach
CoRR, 2017 We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters.
Elad Hazan +2 more
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Hyperparameter Optimization [PDF]
, 2019 Recent interest in complex and computationally expensive machine learning models with many hyperparameters, such as automated machine learning (AutoML) frameworks and deep neural networks, has resulted in a resurgence of research on hyperparameter optimization (HPO). In this chapter, we give an overview of the most prominent approaches for HPO.
Feurer, Matthias, Hutter, Frank
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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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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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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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