检索结果-南通市图书馆

Quantum Contextuality for training neural networks

Chinese Journal of Electronics 2020年第6期29卷 1178-1184页

作者： ZHANG Junwei LI Zhao School of Computer Science and Technology Tianjin University Search Division Alibaba Group

In the training process of neural networks(NNs),the selection of hyper-parameters is crucial,which determines the final training effect of the model.Among them,the Learning rate decay(LRD) can improve the learning speed and accuracy;the Weight decay(WD)improves the over-fitting to varying degrees.However,the decay methods still have problems such as hysteresis and stiffness of parameter adjustment,so that the final model will be inferior.Based on the Quantum contextuality(QC)theory,we propose a Quantum contextuality constraint(QCC) to constrain the weights of nodes in NNs to further improve the training effect.In the simplest classification model,we combine this constraint with different methods of LRD and WD to verify that QCC can further improve the training effect on the decay method.The performance of the experiments shows that QCC can significantly improve the convergence and accuracy of the model.

关键词： Quantum contextuality Non-classical probability constraints training neural networks

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

How Deep Learning networks could be Designed to Locate Mineral Deposits

引用

Journal of Earth Science 2021年第2期32卷 288-292页

作者： Donald A.Singer 10191 N.Blaney Ave. Cupertino 95014CA USA

Whether using a shallow neural network with one hidden layer,or a deep network with many hidden layers,the training data must represent subgroups of the deposit type being explored to be useful.Published examples of neural networks have mostly been limited to one individual mineral deposit for training.Variation of geologic features among deposits within a type are so large that a single deposit cannot provide proper information to train a neural net to generalize and guide exploration for other deposits.Models trained with only one deposit tend to be academic successes but are not of practical value in exploration for other deposits.This is why it takes much experience examining many deposits to properly train an economic geologist—a neural network is not any different.Two examples of shallow neural networks are used to demonstrate the power of neural networks to possibly locate undiscovered deposits and to provide some suggestions of how to deal with missing data.The training data needs to include information spatially related to known deposits and hopefully information from many different deposits of the type.Lessons learned from these and other examples point to a proposed sampling plan for data that could lead to a generalized neural network for exploration.In this plan,10 or more well-explored gold-rich porphyry copper deposits from around the world with 100 or more sample sites near and some distance from each deposit would probably capture important variability among such deposits and provide proper data to train and test a shallow neural network to predict locations of undiscovered deposits.

关键词： porphyry copper training neural networks missing observations

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：