官方离线安装包，测试可用。使用rpm -ivh [rpm完整包名] 进行安装

matlab for循环代码 Tensor-Completion-for-Estimating-Missing-Values-in-Visual-Data Tensor Completion by Python and Numba 本文的算法来自Liu等的两篇论文中的HaLRTC(其余算法会在之后补全) The algorithm in this article is from HaLRTC in two papers by Liu et al.(The rest of the algorithms will be completed later) Matlab 代码来自 感谢你们的工作！ Matlab from Thanks for your work! 上述网址可能无法访问，Matlab源码已经上传至此Repo. The above URL may not be accessible, Matlab source code has been uploaded to this Repo. 本项目旨在用Python实现原作者的算法，并用Numba模块对巨量的for循环进行加速。 Thi

离线安装包，亲测可用

离线安装包，亲测可用

docker-zsh-completion-1.13.1-47.2.gitf43d177.el7.x86_64.rpm

Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively simple but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples. TuckER outperforms all previous state-of-the-art models acrossstandardlinkpredictiondatasets. Weprove that TuckER is a fully expressive model, deriving the bound on its entity and relation embedding dimensionality for full expressiveness which is several orders of magnitude smaller than the bound of previous state-of-the-art models ComplEx and SimplE. We further show that several previously introducedlinearmodelscanbeviewedasspecial cases of TuckER.

论文《Reasoning With Neural Tensor Networks for Knowledge Base Completion》原文及译文

win 系列 使用的minio的exe免安装使用版本

Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm 论文实现代码matlab

Matrix completion和Tensor completion的经典算法 OptSpace和TenALS的源码 包含C的实现和Matlab的实现