Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics 文章梳理的ppt

Building a Scalable Bipartite P2P Overlay Network

ud859 使用 Google App Engine 类构建可扩展应用程序的课程代码

Scalable IO in Java.pdf

2017 SecureML：A system for scalable privacy-preserving machine learning论文ppt

钙钛矿文献

google EfficientDet 算法中文版paper.将高效网络骨架与我们提出的BiFPN 和复合尺度相结合，我们开发了一种新的对象检测器家族，称为高效Det，它始终以比以前的对象检测器更少的参数和FLOP 来获得更好的精度。图和图形显示COCO 数据集上的性能比较。在类似的精度约束下，我们的有效DET使用的FLOP 比YOLOv3 少28 倍，FLOP 比RetinaNet 少30倍，FLOP 比最近基于ResNet 的NAS-FPN 少19 倍。特别是，在单模型和单测试时间尺度下，我们的高效Det-D7 实现了最先进的53.7AP 和52M 参数和325B FLOP，在1.5AP的情况下优于以前最好的检测器，而在4 倍小和使用13 倍少的FLOP。我们的高效DET 在GPU/CPU 上也比以前的检测器快4 倍至11 倍。

TR-SDTN: Trust Based Efficient and Scalable Routing in Hostile Social DTNs

陈天奇xgb论文。Tree boosting is a highly eective and widely used machine learning method. In this paper, we describe a scalable endto- end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

Scalable Data Center Network Architecture With Distributed Placement of Optical Switches and Racks