Results 261 to 270 of about 1,858,266 (298)

Hate speech detection using pretrained DeBERTaV3 optimized with Grey Wolf Optimizer: a hybrid approach for social media content moderation. [PDF]

Sci Rep
Das MK, Skaria B, Auddy A, Vidhyalakshmi M.   +3 more
europepmc   +1 more source

Intelligent kinematic physics engine construction for hyper-redundant cable-driven flexible manipulator. [PDF]

PLoS One
Wang S, Wang P, Wang X, Chen S, Liu M.
europepmc   +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

Learning hyperparameter optimization initializations

2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015
Hyperparameter optimization is often done manually or by using a grid search. However, recent research has shown that automatic optimization techniques are able to accelerate this optimization process and find hyperparameter configurations that lead to better models.
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme   +2 more
openaire   +2 more sources

Hyperparameter optimization in learning systems

Journal of Membrane Computing, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +3 more sources