.png){kind=icon}
返回
Optimizing data acquisition: a Bayesian approach for efficient machine learning model training
.png){kind=icon}
OA
AI
DOI：10.1088/2632-2153/ad605f.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Acquiring a substantial number of data points for training accurate machine learning (ML) models is a big challenge in scientific fields where data collection is resource-intensive. Here, we propose a novel approach for constructing a minimal yet highly informative database for training ML models in complex multi-dimensional parameter spaces. To achieve this, we mimic the underlying relation between the output and input parameters using Gaussian process regression (GPR). Using a set of known data, GPR provides predictive means and standard deviation for the unknown data. Given the predicted standard deviation by GPR, we select data points using Bayesian optimization to obtain an efficient database for training ML models. We compare the performance of ML models trained on databases obtained through this method, with databases obtained using traditional approaches. Our results demonstrate that the ML models trained on the database obtained using Bayesian optimization approach consistently outperform the other two databases, achieving high accuracy with a significantly smaller number of data points. Our work contributes to the resource-efficient collection of data in high-dimensional complex parameter spaces, to achieve high precision ML predictions.
Keyword:
machine learning
data acquisition
Bragg gratings
Bayesian optimization
FDTD simulations
inverse design
optimal data generation
期刊
M
IF:
4.6
论文数:
1.1K
被引数:
3.4K
机构
引用论文
Taking the Human Out of the Loop: A Review of Bayesian Optimization将人类带出循环: 贝叶斯优化的回顾.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏Training of photonic neural networks through in situ backpropagation and gradient measurement通过原位反向传播和梯度测量训练光子神经网络OPTICA
OAAI
OPTICAIF8.5
OAAI分享收藏Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale智能纳米光子学: 在纳米尺度上融合光子学和人工智能NANOPHOTONICSOAAI
NANOPHOTONICSIF6.6
OAAI分享收藏分享收藏Deep learning for accelerated all-dielectric metasurface design用于加速全电介质超表面设计的深度学习OPTICS EXPRESSOAAI
OPTICS EXPRESSIF3.3
OAAI分享收藏A review of developments in near infrared methane detection based on tunable diode laser基于可调谐二极管激光器的近红外甲烷检测研究进展
PREAI
分享收藏
查看更多内容