发布求助
文献互助 智能选刊 最新文献

Journal of Machine Learning for Modeling and Computing最新文献

筛选
英文 中文
Constrained Gaussian process regression: an adaptive approach for the estimation of hyperparameters and the verification of constraints with high probability 约束高斯过程回归:一种用于超参数估计和高概率约束验证的自适应方法
Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2021039837
G. Perrin, S. Veiga
{"title":"Constrained Gaussian process regression: an adaptive approach for the estimation of hyperparameters and the verification of constraints with high probability","authors":"G. Perrin, S. Veiga","doi":"10.1615/jmachlearnmodelcomput.2021039837","DOIUrl":"https://doi.org/10.1615/jmachlearnmodelcomput.2021039837","url":null,"abstract":"This paper focuses on the Gaussian Process regression (GPR) of non-linear functions subject to multiple linear constraints, such as boundedness, monotonicity or convexity. It presents an algorithm allowing to optimize, in a concerted way, the statistical moments of the Gaussian process used for the regression, and the position of a reduced number of points where the constraints are required to hold, such that the constraints are verified in the whole input space, with high probability, at a reasonable computational cost. After having presented the theoretical bases and the numerical implementation of this algorithm, this paper illustrates its efficiency though the analysis of several test functions of increasing dimensions.","PeriodicalId":320604,"journal":{"name":"Journal of Machine Learning for Modeling and Computing","volume":"150 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130905157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The Role of Stiffness in Training and Generalization of ResNets 刚度在ResNets训练和泛化中的作用
Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2023047131
Joshua Hudson, Marta D Elia, H. Najm, K. Sargsyan
{"title":"The Role of Stiffness in Training and Generalization of ResNets","authors":"Joshua Hudson, Marta D Elia, H. Najm, K. Sargsyan","doi":"10.1615/jmachlearnmodelcomput.2023047131","DOIUrl":"https://doi.org/10.1615/jmachlearnmodelcomput.2023047131","url":null,"abstract":".","PeriodicalId":320604,"journal":{"name":"Journal of Machine Learning for Modeling and Computing","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124835385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Machine Learning for Predicting the Dynamics of Infectious Diseases during travel through Physics Informed Neural Networks 通过物理信息神经网络预测旅行中传染病动态的机器学习
Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2023047213
Alonso Ogueda, E. Martinez, VISWANATHAN ARUNACHALAM, P. Seshaiyer
{"title":"Machine Learning for Predicting the Dynamics of Infectious Diseases during travel through Physics Informed Neural Networks","authors":"Alonso Ogueda, E. Martinez, VISWANATHAN ARUNACHALAM, P. Seshaiyer","doi":"10.1615/jmachlearnmodelcomput.2023047213","DOIUrl":"https://doi.org/10.1615/jmachlearnmodelcomput.2023047213","url":null,"abstract":"In the past few years, approaches such as physics informed neural networks (PINNs) have been applied to a variety of applications that can be modeled by linear and nonlinear ordinary and partial differential equations. Specifically, this work builds on the application of PINNs to a SIRD (susceptible, infectious, recovered, and dead) compartmental model and enhances it to build new mathematical models that incorporate transportation between populations and their impact on the dynamics of infectious diseases. Our work employs neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters. We show how these approaches are capable of predicting the behavior of a disease described by governing differential equations that include parameters and variables associated with the movement of the population between neighboring cities. We show that our model validates real data and also how such PINNs based methods predict optimal parameters for given datasets.","PeriodicalId":320604,"journal":{"name":"Journal of Machine Learning for Modeling and Computing","volume":"32 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123518194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
首页 上一页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信
小红书