论文阅读ICCV_2019_paper How Do Neural Networks See Depth in Single Images相关ppt

半监督学习以改善肺癌的检测 使用生成模型和半监督学习促进肺癌检测 用于训练的数据集 LUNA16数据集（ ） Kaggle数据科学碗2017（ ） 建筑学 结果 结节检测器结果 发电机结果 分类器结果 方法 准确性 监督学习 64％ 半监督学习 87.3％ 资源 Kaggle数据科学碗2017内核 Luna2016-肺结节检测 Tensorflow中的半监督学习GAN [链接] DSB2017 [链接] Keras-GAN [链接] 使用很少的数据构建强大的图像分类模型[link] 贡献者： Dhamodhran（ @ svella9 ） 悉达思R科蒂（ siddharthkoti ） 维杰·蒙达拉吉（ Vijay-Mundaragi ）

介绍 一维（1D）信号/时间序列数据上的多个SOTA骨干深度神经网络（例如ResNet [1]，ResNeXt [2]，RegNet [3]）的PyTorch实现。 如果您在工作中使用此代码，请引用我们的论文 @inproceedings{hong2020holmes, title={HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units}, author={Hong, Shenda and Xu, Yanbo and Khare, Alind and Priambada, Satria and Maher, Kevin and Aljiffry, Alaa and Sun, Jimeng and Tumanov, Alexey}, bookt

双水平图神经网络 总览 此存储库包含用于药物相互作用预测的双层图神经网络的代码。 有关详细信息，请参阅我们的论文 用于药物相互作用预测的双层图神经网络。 白云生*，顾坚*，孙宜州，王伟。 ICML 2020图形表示学习及超越（GRL +）研讨会 。 模型 我们引入Bi-GNN建模生物链接预测任务，例如药物-药物相互作用（DDI）和蛋白质-蛋白质相互作用（PPI）。 以药物-药物相互作用为例，使用机器学习的现有方法要么仅利用药物之间的链接结构而不使用每个药物分子的图形表示，要么仅利用单个药物化合物的结构而不对高级DDI使用图形结构图形。 我们方法的关键思想是从根本上将数据视为双层图，其中最高

Data Networks (2nd Edition) by Dimitri P. Bertsekas and Robert G. Gallager 英文原著

Data Networks (2nd Edition) by Dimitri P. Bertsekas and Robert G. Gallager 英文原著

Computer Networks Andrew S,Tanenbaum计算机网络第四版英文原版

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas. Table of Contents Chapter 1 Introduction Chapter 2 Complex Networks Chapter 3 Machine Learning Chapter 4 Network Construction Techniques Chapter 5 Network-Based Supervised Le

深度排序 学习使用seq2seq模型对数字进行排序。 运行这段代码 调用pip install -r requirements.txt安装所有依赖项。 产生资料 可以使用所有数据 样品电话 python generate.py \ --name="train" \ --size=10000 \ --max_val=256 \ --min_length=2 \ --max_length=256 \ 训练 可以通过在设置适当的参数，然后将train.run()设置为在调用，最后一次调用python main.py （是的，我很抱歉，对于未配置命令行参数）。 从上面的示例调用生成的数据集中训练了1个纪元，大约花费了10分钟。 评估 在train.txt上训练模型后，使用生成测试集（ name="test" ），然后以与上所述相同的方式运行 ，以查看该模型的一些示例评估。 再

As internetworking technologies continue to pervade nearly every aspect of public and private industry worldwide, the demand grows for individuals who can demonstrate they possess the skills needed to manage these technologies. Recognizing this need, Juniper Networks—the lead- ing provider of Internet infrastructure solutions that enable ISPs and other telecommunications companies to meet the demands of Internet growth—recently restructured its certification program to provide a clear path for the acquisition of these skills. Sybex is proud to have part- nered with the Juniper Networks and worked closely with members of the Juniper Networks Technical Certification Program to develop this Official Study Guide for the Juniper Networks Certified Internetworking Associate certification.