Results 201 to 210 of about 1,862,731 (241)

Meta-Learning for Hyperparameter Optimization

Jincheng Zhang
openalex   +1 more source

Modeling and Performance Analysis of a Liquid Desiccant Cooling and Dehumidification System Using ITSO-TCN-BiGRU-SA. [PDF]

Sensors (Basel)
Ou X, Wang X, Wang Z.
europepmc   +1 more source

From QSAR to deep learning: an interpretable comprehensive pipeline with a read-across approach for mutagenicity prediction <i>via</i> the Enalos Cloud Platform. [PDF]

RSC Adv
Varsou DD, Tsoumanis A, Longhin EM, Dusinska M, Mintis DG, Kolokathis PD, Papavasileiou KD, Lynch I, Melagraki G, Afantitis A.   +9 more
europepmc   +1 more source

Machine learning prediction and hybrid GRA-AHP optimization of AWJM parameters of ultrasonic assisted stir cast Al6061-B[Formula: see text]C-ZrO[Formula: see text] composites. [PDF]

Sci Rep
K A, Shaik AS, Tota RK, Chavda NK, Bodduru K, Alqahtani SS, Kassa MK.   +6 more
europepmc   +1 more source

A novel deep learning prediction method for PM2.5 integrating improved MSTL decomposition and FATA optimization algorithm. [PDF]

PLoS One
Zhao X, Qiao P, Bharti B, Li H, Yang S, Shi Q, Qin Q, Chen J.   +7 more
europepmc   +1 more source

Title: Adaptive Hyperparameter Optimization

Jincheng Zhang
openalex   +1 more source

Gradient-Based Optimization of Hyperparameters

Neural Computation, 2000
Many machine learning algorithms can be formulated as the minimization of a training criterion that involves a hyperparameter. This hyperparameter is usually chosen by trial and error with a model selection criterion. In this article we present a methodology to optimize several hyper-parameters, based on the computation of the gradient of a model ...
Yoshua Bengio
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Reproducible Hyperparameter Optimization

Journal of Computational and Graphical Statistics, 2021
A key issue in machine learning research is the lack of reproducibility. We illustrate what role hyperparameter search plays in this problem and how regular hyperparameter search methods can lead t...
Lars Hertel, Pierre Baldi, Daniel L. Gillen   +2 more
openaire   +2 more sources

Hyperparameter Optimization Machines

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016
Algorithm selection and hyperparameter tuning are omnipresent problems for researchers and practitioners. Hence, it is not surprising that the efforts in automatizing this process using various meta-learning approaches have been increased. Sequential model-based optimization (SMBO) is ne of the most popular frameworks for finding optimal hyperparameter
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme   +2 more
openaire   +1 more source

Automated machine learning hyperparameters tuning through meta-guided Bayesian optimization

Progress in Artificial Intelligence
International audienceThe selection of one or more optimized Machine Learning (ML) algorithms and the configuration of significant hyperparameters are among the crucial but challenging tasks for the advanced data analytics using ML methodologies. However,
Mourad Bouneffa, Moncef Garouani
exaly   +2 more sources