Large-Scale Learnable Graph Convolutional Networks（LGCN）论文的ppt pdf 资源分享

在大规模标记属性图上优化算法的研究很少。在这里，我们提出了两种优化方法，并将它们与直接查询图数据库的简单方法进行了比较。我们工作的目的是确定像Neo4j这样的图形数据库的限制因素，我们描述了一个解决这些挑战的新方案。

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Large Scale Machine Learning with Python

ImageNet Large Scale Visual Recognition Challenge.pdf

In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3×3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Large Scale Distributed Deep Networks

Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods

CFS: A Distributed File System for Large Scale Container Platforms

对于一类具有未知时变延迟的大型系统，考虑了分散的输出反馈控制问题。 不确定的互连受系数未知的一般非线性函数限制。 每个子系统的控制方向参数都是未知的，这给分散控制器设计带来了挑战。 为了解决这个问题，我们在Nussbaum函数的帮助下提出了一种新的分散控制方案。 首先设计分散式滤波器。 通过构造Lyapunov-Krasovskii函数，我们设计了动态输出反馈控制器。 严格证明了闭环系统是渐近稳定的。 最后，进行了仿真，结果验证了所提方法的有效性。