Results 51 to 60 of about 38,367 (259)

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

Optimization of Annealed Importance Sampling Hyperparameters

, 2023
AbstractAnnealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. Although AIS is guaranteed to provide unbiased estimate for any set of hyperparameters, the common implementations rely on simple heuristics such as the geometric average bridging distributions between ...
Shirin Goshtasbpour, Fernando Pérez-Cruz   +1 more
openaire   +3 more sources

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

Advanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang, Adrian Ehrenhofer, Thomas Wallmersperger   +2 more
wiley   +1 more source

Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization

CoRR, 2022
Bayesian Optimization (BO) is a common solution to search optimal hyperparameters based on sample observations of a machine learning model. Existing BO algorithms could converge slowly even collapse when the potential observation noise misdirects the optimization.
Lei Cui, Yangguang Li 0001, Xin Lu, Dong An 0002, Fenggang Liu   +4 more
openaire   +3 more sources

BAYESIAN OPTIMIZATION FOR TUNING HYPERPARAMETRS OF MACHINE LEARNING MODELS: A PERFORMANCE ANALYSIS IN XGBOOST

Компютерні системи та інформаційні технології
The performance of machine learning models depends on the selection and tuning of hyperparameters. As a widely used gradient boosting method, XGBoost relies on optimal hyperparameter configurations to balance model complexity, prevent overfitting, and ...
Микола ЗЛОБІН, Володимир БАЗИЛЕВИЧ   +1 more
doaj   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Hyperparameter Optimization for Effort Estimation

CoRR, 2018
Software analytics has been widely used in software engineering for many tasks such as generating effort estimates for software projects. One of the "black arts" of software analytics is tuning the parameters controlling a data mining algorithm. Such hyperparameter optimization has been widely studied in other software analytics domains (e.g.
Tianpei Xia, Rahul Krishna, Jianfeng Chen, George Mathew, Xipeng Shen, Tim Menzies   +5 more
openaire   +2 more sources

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

Advanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source

Implantable Ionic Memristors Based on Natural Polymer Heterojunctions

Advanced Functional Materials, EarlyView.
We report an implantable natural polymer‐based ionic memristor composed of hyaluronic acid, chitosan, and PDMS. The device achieved 98.94% accuracy in MNIST classification while reducing training time by 36.8% compared with a conventional artificial neural network (ANN).
Dong‐yup Lee, Byung Ik Park, Gihyeon Yim, Hyunjae Yoo, Sangjun Ma, Jundong Park, Jeong‐Yun Sun   +6 more
wiley   +1 more source