Results 31 to 40 of about 211,846 (266)
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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Handwritten Digit Recognition: Hyperparameters-Based Analysis
Applied Sciences, 2020 Neural networks have several useful applications in machine learning. However, benefiting from the neural-network architecture can be tricky in some instances due to the large number of parameters that can influence performance.
Saleh Albahli +3 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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MathematicsLoad forecasting is an integral part of the power industries. Load-forecasting techniques should minimize the percentage error while prediction future demand. This will inherently help utilities have an uninterrupted power supply.
Umme Mumtahina +2 more
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On Architecture Selection for Linear Inverse Problems with Untrained Neural Networks
Entropy, 2021 In recent years, neural network based image priors have been shown to be highly effective for linear inverse problems, often significantly outperforming conventional methods that are based on sparsity and related notions.
Yang Sun +2 more
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Clustering via kernel decomposition [PDF]
, 2006 Spectral clustering methods were proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this letter, the affinity matrix is created from the elements of a nonparametric density estimator and then decomposed to obtain ...
Girolami, M. +2 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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Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image [PDF]
, 2013 Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction
Benediktsson, Jon Atli +3 more
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Is one hyperparameter optimizer enough? [PDF]
Proceedings of the 4th ACM SIGSOFT International Workshop on Software Analytics, 2018 Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, there has not been much discussion on which hyperparameter tuner is best for software analytics.
Tu, Huy, Nair, Vivek
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Frontiers in Artificial IntelligenceA progressive brain disease that affects memory and cognitive function is Alzheimer’s disease (AD). To put therapies in place that potentially slow the progression of AD, early diagnosis and detection are essential.
A. S. Elmotelb +5 more
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