Network Architecture Search Survey

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目录
相关文章
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设计模式 缓存 监控
译|Design patterns for container-based distributed systems(下)
译|Design patterns for container-based distributed systems(下)
68 0
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机器学习/深度学习 存储 传感器
Automated defect inspection system for metal surfaces based on deep learning and data augmentation
简述:卷积变分自动编码器(CVAE)生成特定的图像,再使用基于深度CNN的缺陷分类算法进行分类。在生成足够的数据来训练基于深度学习的分类模型之后,使用生成的数据来训练分类模型。
151 0
《The 8 Neural Network Architectures Machine Learning Resarchers Need to Learn》电子版地址
The 8 Neural Network Architectures Machine Learning Resarchers Need to Learn
82 0
《The 8 Neural Network Architectures Machine Learning Resarchers Need to Learn》电子版地址
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搜索推荐 PyTorch 算法框架/工具
Re30:读论文 LegalGNN: Legal Information Enhanced Graph Neural Network for Recommendation
Re30:读论文 LegalGNN: Legal Information Enhanced Graph Neural Network for Recommendation
Re30:读论文 LegalGNN: Legal Information Enhanced Graph Neural Network for Recommendation
Re8:读论文 Hier-SPCNet: A Legal Statute Hierarchy-based Heterogeneous Network for Computing Legal Case
Re8:读论文 Hier-SPCNet: A Legal Statute Hierarchy-based Heterogeneous Network for Computing Legal Case
Re8:读论文 Hier-SPCNet: A Legal Statute Hierarchy-based Heterogeneous Network for Computing Legal Case
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机器学习/深度学习 移动开发 自然语言处理
Paper:《Graph Neural Networks: A Review of Methods and Applications》翻译与解读
Paper:《Graph Neural Networks: A Review of Methods and Applications》翻译与解读
Paper:《Graph Neural Networks: A Review of Methods and Applications》翻译与解读
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SQL 编译器 API
Efficiently Compiling Efficient Query Plans for Modern Hardware 论文解读
这应该是SQL查询编译的一篇经典文章了,作者是著名的Thomas Neumann,主要讲解了TUM的HyPer数据库中对于CodeGen的应用。 在morsel-driven那篇paper 中,介绍了HyPer的整个执行框架,会以task为单位处理一个morsel的数据,而执行的处理逻辑(一个pipeline job)就被编译为一个函数。这篇paper则具体讲如何实现动态编译。
435 0
Efficiently Compiling Efficient Query Plans for Modern Hardware 论文解读
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机器学习/深度学习 人工智能 编解码
Paper:《Graph Neural Networks: A Review of Methods and Applications》解读(二)
Paper:《Graph Neural Networks: A Review of Methods and Applications》
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机器学习/深度学习 数据可视化 数据挖掘
Paper:《Graph Neural Networks: A Review of Methods and Applications》解读(一)
Paper:《Graph Neural Networks: A Review of Methods and Applications》