Diagnosing scientific replicability through probabilistic distinguishability.

Docker light image based on nodejs 16 alphine with custom tools installed: aws cli.

A general purpose scientific computing environment for hydrologic applications.

Demo service for predicting real-time Click Through Rate prediction using XgBoost.

论文：https://arxiv.org/pdf/2003.14247.pdf 源代码：https://github.com/megvii-research/DPGN 目录 核心内容 分布级关系 DPGN网络结构及实现算法 DPGN网络结构分析 Instance Similarities(实例相似度

14 Just wondering if anyone has any information on the status of project Rassilon, Neo4j's side project which focuses on improving horizontal scalabi

题目广义多实例学习的判别概率框架（DiscriminativeProbabilisticFrameworkforGeneralizedMulti-InstanceLearning）Bib@inproceedings{Anh:2018:22812285,author={AnhTPhamandRaviv

EFFICIENT PROBABILISTIC LOGIC REASONING WITH GRAPH NEURAL NETWORKS（使用神经网络进行有效的概率逻辑推理） 原文连接 介绍： 由于知识图谱存在不正确、不完整或者重复的数据，因此对知识图谱进行补全推理非常重要。文章使用了马尔科夫逻辑网络