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simple-effective-text-matching

Source code of the ACL2019 paper "Simple and Effective Text Matching with Richer Alignment Features".

speaker_follower

Code release for Fried et al., Speaker-Follower Models for Vision-and-Language Navigation. in NeurIPS, 2018.

springmvc-activiti5.16-shiro

基于SpringMVC+Activiti 5.16 的简单OA，可以用作模版供以后学习用。

sr

stock-knowledge-graph

利用网络上公开的数据构建一个小型的证券知识图谱/知识库

streetlearn

A C++/Python implementation of the StreetLearn environment based on images from Street View, as well as a TensorFlow implementation of goal-driven navigation agents solving the task published in “Learning to Navigate in Cities Without a Map”, NeurIPS 2018

submit

tagan

An official PyTorch implementation of the paper "Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language", NeurIPS 2018

tdengine

An open-source big data platform designed and optimized for the Internet of Things (IoT).

tpprl

Code and data for "Deep Reinforcement Learning of Marked Temporal Point Processes", NeurIPS 2018

training

uestc_internet_plus_course_project

本人在大学期间所有课程课设和作业的代码和部分报告，包括【计算机组成与结构】、【计算机网络与通信技术】、【软件基础综合课程设计】、【互联网软件开发综合课程设计】、【数据挖掘与大数据分析】、【时间序列分析】、【机器学习】、【数据结构与算法】、【并行程序设计导论】、【计算机操作系统】、【计算机视觉】

understanding-the-modeling-of-network-delays-using-nn

Recent trends in networking are proposing the use of Machine Learning (ML) techniques for the control and operation of the network. In this context, ML can be used as a computer network modeling technique to build models that estimate the network performance. Indeed, network modeling is a central technique to many networking functions, for instance in the field of optimization, in which the model is used to search a configuration that satisfies the target policy. In this paper, we aim to provide an answer to the following question: Can neural networks accurately model the delay of a computer network as a function of the input traffic? For this, we assume the network as a black-box that has as input a traffic matrix and as output delays. Then we train different neural networks models and evaluate its accuracy under different fundamental network characteristics: topology, size, traffic intensity and routing. With this, we aim to have a better understanding of computer network modeling with neural nets and ultimately provide practical guidelines on how such models need to be trained.

vue2-elm

基于 vue2 + vuex 构建一个具有 45 个页面的大型单页面应用

websocket_libevent_threadpool

基于Libevent库与线程池编写的简单WebSocket服务器与客服端