VLSI Physical Design:From Graph Partitioning to Timing Closure pdf
2019-12-21 21:03:57 6.11MB CAD physical
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立体匹配是深度估计的基础。而bp算法和graph cut算法是比较成功的解决立体匹配(全局)的算法,这里有几种经典的基准源代码,以及代码使用方法及部分注释。一些小的改变甚至于可以不需要知道算法步骤即可实现。
2019-12-21 21:02:14 13.65MB 立体匹配 BP graph-cut stereo-matching
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在unity3d中实现复杂图标的绘制,包括折线图,饼状图,柱状图等
2019-12-21 21:01:45 3.68MB NGUI 图表工具
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Graph Maker V 1.5.8 unity插件。画折线图、饼状图、柱状图
2019-12-21 20:56:28 2.67MB unity graphmaker 1.5.8
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multi-label graph cut,image segmentation 。
2019-12-21 20:36:01 68KB multi-label graph cut,image segmentation
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unity强大的图表插件,Graph And Chart 1.6,处理数据
2019-12-21 20:35:12 28.52MB Graph
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Graph Algorithms by Mark Needham and Amy E. Hodler Copyright © 2019 Amy Hodler and Mark Needham. All rights reserved. What’s in This Book This book is a practical guide to getting started with graph algorithms for developers and data scientists who have experience using Apache Spark™ or Neo4j. Although our algorithm examples utilize the Spark and Neo4j platforms, this book will also be helpful for understanding more general graph concepts, regardless of your choice of graph technologies. The first two chapters provide an introduction to graph analytics, algorithms, and theory. The third chapter briefly covers the platforms used in this book before we dive into three chapters focusing on classic graph algorithms: pathfinding, centrality, and community detection. We wrap up the book with two chapters showing how graph algorithms are used within workflows: one for general analysis and one for machine learning. At the beginning of each category of algorithms, there is a reference table to help you quickly jump to the relevant algorithm. For each algorithm, you’ll find: • An explanation of what the algorithm does • Use cases for the algorithm and references to where you can learn more • Example code providing concrete ways to use the algorithm in Spark, Neo4j, or both 图方法方面最新的参考书,本文理论实践兼备(看标题就知道了),内容高清无码书签完整诚不我欺,强烈推荐给需要的朋友!
2019-12-21 20:24:10 10.86MB Graph Algorithm Apache Spark
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Domain Specific Knowledge Graph Construction, 领域特定知识图构建(KGC)是一个活跃的研究领域,最近由于机器学习技术(如深度神经网络和单词嵌入)取得了令人印象深刻的进展。本书将以一种引人入胜和可访问的方式综合Web数据上的知识图结构。
2019-12-21 20:23:58 2.47MB 知识图谱 KG knowle 领域知识图谱
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图论入门书籍,包括中文版和英文版本,pdf的,比较全面
2019-12-21 20:18:41 29.55MB 图论 应用
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Efficient Graph-Based Image Segmentation & k-means Image Segmentation
2019-12-21 20:16:29 1KB image segmen
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