Name: Spartacus
Type: User
Company: University of Chinese Academy of Sciences
Bio: PhD candidate in University of Chinese Academy of Sciences.
Location: 1 Yanqihu East Road, Huairou District, Beijing
Blog: https://www.ucas.ac.cn/
Spartacus's Projects
OSSEM Detection Model
用PaddlePaddle和Tensorflow实现常用的深度学习算法
一个具有划词翻译功能的跨平台pdf阅读器,用着挺好用开源一下造福众科研人员,欢迎star
A simple utility to classify packets into flows. It's so simple that only one task is aimed to finish. For Deep Packet Inspection or flow classification, it's so common to analyze the feature of one specific flow. I have make the attempt to use made-ready tools like tcpflows, tcpslice, tcpsplit, but all these tools try to either decrease the trace volume (under requirement) or resemble the packets into flow payloads (over requirement). I have not found a simple tool to classify the packets into flows without further processing. This is why this program is born.
Semi-supervised learning with graph embeddings
Various software built on various platforms.
Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)
Graph Convolutional Networks in PyTorch
A Python Library for Graph Outlier Detection (Anomaly Detection)
Python 进阶学习笔记
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
pytorch handbook是一本开源的书籍,目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门,其中包含的Pytorch教程全部通过测试保证可以成功运行
Geometric Deep Learning Extension Library for PyTorch
PyTorch 官方中文教程包含 60 分钟快速入门教程,强化教程,计算机视觉,自然语言处理,生成对抗网络,强化学习。欢迎 Star,Fork!
ETW Python Library
A tutorial and implement of disease centered Medical knowledge graph and qa system based on it。知识图谱构建,自动问答,基于kg的自动问答。以疾病为中心的一定规模医药领域知识图谱,并以该知识图谱完成自动问答与分析服务。
论文写作与资料分享
A list of data mining and machine learning papers that I implemented in 2019.
Ring-Log是一个高效简洁的C++异步日志, 其特点是效率高(每秒支持至少125万+日志写入)、易拓展,尤其适用于频繁写日志的场景
A scalable Gensim implementation of "Learning Role-based Graph Embeddings" (IJCAI 2018).
Core streaming heterogeneous graph clustering and anomaly detection code (KDD 2016)
A robust and noisy-resilient anomaly detection method by explicitly learning the representations of time-invariant and time-varying characteristics of multivariate KPIs
安全思维导图集合
记录我的安全学术学习 太难了 干啥都不容易
PyTorch implementation of SENet