Results 221 to 230 of about 43,032 (264)

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Enhanced Dhole Optimization Algorithm for hyperparameter tuning of a TCN-BiGRU-MHA hybrid architecture for wind power forecasting. [PDF]

Sci Rep
Lale T.
europepmc   +1 more source

A manta ray-bayesian optimization approach for hyperparameter-tuned convolutional neural networks in lung cancer classification. [PDF]

Sci Rep
Samal S, Sunder S, Gadekellu TR, Yagin FH, Shawi RE, Ahmed N.   +5 more
europepmc   +1 more source

Machine Learning-Based Prediction of Textural Properties and Nonlinear Regulatory Pattern Analysis of 3D-Printed Dough Containing Konjac Glucomannan. [PDF]

Foods
Leng W, Sun Y, Xie J, Pang J.
europepmc   +1 more source

Stochastic Grey Wolf Optimization for Hyperparameter Tuning of LSTM and RNN Models in Energy Forecasting. [PDF]

Sci Rep
Albser OA, Mouhamed MR, Shatta SA, Sweilem NH, Darwish A.   +4 more
europepmc   +1 more source

A Hybrid CNN-GRU-SE Forecasting Method for Short-Term Photovoltaic Power Considers AFD and Data Aggregation. [PDF]

Entropy (Basel)
Liu K, Jia D, Zhan H, Zhou J, Wang Z, Bao J.   +5 more
europepmc   +1 more source

Machine learning-based classification of COVID-19 severity using respiratory microbiome profiles from shotgun metagenomic sequencing. [PDF]

Front Bioinform
Avina-Bravo EG, García-Lorenzo I, Alfaro-Ponce M, Breton-Deval L.   +3 more
europepmc   +1 more source

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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   +1 more source

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

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 ...
openaire   +2 more sources