Results 251 to 260 of about 42,332 (292)

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Metaheuristic-optimized interaction-aware deep learning with large language model assistance for data-driven water quality prediction. [PDF]

Sci Rep
Mattar EA, El-Kenawy EM, Alhammad SM, Eid MM.   +3 more
europepmc   +1 more source

Metaheuristic optimization of deep CNNs for multi-class diagnosis of cervical cancer and lymphoma. [PDF]

Sci Rep
Abdelhay EH, Elgamily KM, Badr WOE.
europepmc   +1 more source

Population-level migration modeling of North America's birds through data integration with BirdFlow. [PDF]

Mov Ecol
Chen Y, Slager DL, Plunkett E, Fuentes M, Deng Y, Mackenzie SA, Berrigan LE, Fink D, Sheldon D, Van Doren BM, Dokter AM.   +10 more
europepmc   +1 more source

Benchmarking the performance of uncertainty quantification methods for neural network-based interatomic potentials. [PDF]

J Cheminform
Wimer NT, Mueller J, Hamel S, Lordi V.
europepmc   +1 more source

Hyperparameter Tuning for Machine and Deep Learning with R [PDF]

, 2023
This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods.
exaly   +2 more sources

Fast hyperparameter tuning using Bayesian optimization with directional derivatives

Knowledge-Based Systems, 2020
Fast hyperparameter tuning using Bayesian optimization with directional ...
Tinu Theckel Joy, Santu Rana, Sunil Gupta   +2 more
exaly   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Hyperparameter Tuning of ConvLSTM Network Models

2021 44th International Conference on Telecommunications and Signal Processing (TSP), 2021
Deep learning algorithms have achieved amazing performance in computer vision area. However, a biggest problem deep learning has, is the high dependency on hyper-parameters. The algorithm results may be different, depending on hyper-parameters. This paper presents an effective method for hyper-parameter tuning using deep learning.
Roberta Vrskova, Peter Sykora, Patrik Kamencay, Róbert Hudec, Roman Radil   +4 more
openaire   +1 more source

Beyond Manual Tuning of Hyperparameters

KI - Künstliche Intelligenz, 2015
The success of hand-crafted machine learning systems in many applications raises the question of making machine learning algorithms more autonomous, i.e., to reduce the requirement of expert input to a minimum. We discuss two strategies towards this goal: (1) automated optimization of hyperparameters (including mechanisms for feature selection ...
Frank Hutter, Jörg Lücke, Lars Schmidt-Thieme   +2 more
openaire   +2 more sources

Game AI hyperparameter tuning in rinascimento

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019
Hyperparameter tuning is an important mixed-integer optimisation problem, especially in the context of real-world applications such as games. In this paper, we propose a function suite around hyperparameter optimisation of game AI based on the card game Splendor and using the Rinascimento framework.
Ivan Bravi, Vanessa Volz, Simon M. Lucas
openaire   +1 more source