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流行的张量列(TT)和张量环(TR)分解在科学和工程上取得了很有前途的结果。然而,TT和TR分解只是建立相邻两个因子之间的联系,并且对张量模的排列高度敏感,导致了不充分和不灵活的表示。本文提出了一种广义张量分解,它将一个N阶张量分解为一组n阶因子,并建立了任意两个因子之间的多线性运算/联系。由于它可以图形化地解释为所有因素的全连接网络,我们将其命名为全连接张量网络(FCTN)分解。FCTN分解的优点在于充分刻画任意两个张量模间的内在相关性和换位的本质不变性。此外,我们将FCTN分解应用于一个有代表性的任务,即张量补全,并提出一个有效的基于近端交替最小化的算法。在理论上,我们证明了该算法的收敛性,即得到的算法序列全局收敛于一个临界点。实验结果表明,该方法与现有的基于张量分解的方法相比具有良好的性能。

https://qibinzhao.github.io/publications/AAAI2021_Yu_Bang_Zheng/AAAI2021_FCTN_Decomposition_ybz.pdf

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Social media platforms like Facebook, Twitter, and Instagram have enabled connection and communication on a large scale. It has revolutionized the rate at which information is shared and enhanced its reach. However, another side of the coin dictates an alarming story. These platforms have led to an increase in the creation and spread of fake news. The fake news has not only influenced people in the wrong direction but also claimed human lives. During these critical times of the Covid19 pandemic, it is easy to mislead people and make them believe in fatal information. Therefore it is important to curb fake news at source and prevent it from spreading to a larger audience. We look at automated techniques for fake news detection from a data mining perspective. We evaluate different supervised text classification algorithms on Contraint@AAAI 2021 Covid-19 Fake news detection dataset. The classification algorithms are based on Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT). We also evaluate the importance of unsupervised learning in the form of language model pre-training and distributed word representations using unlabelled covid tweets corpus. We report the best accuracy of 98.41\% on the Covid-19 Fake news detection dataset.

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