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Guaranteed Scalable Learning of Latent Tree Models., , , , , and . UAI, volume 115 of Proceedings of Machine Learning Research, page 883-893. AUAI Press, (2019)SPARTan: Scalable PARAFAC2 for Large & Sparse Data., , , , , , and . KDD, page 375-384. ACM, (2017)TASTE: temporal and static tensor factorization for phenotyping electronic health records., , , , , , , and . CHIL, page 193-203. ACM, (2020)Model-Driven Sparse CP Decomposition for Higher-Order Tensors., , , , and . IPDPS, page 1048-1057. IEEE Computer Society, (2017)COPA: Constrained PARAFAC2 for Sparse & Large Datasets., , , , , and . CIKM, page 793-802. ACM, (2018)Sparse Hierarchical Tucker Factorization and its Application to Healthcare., , , and . CoRR, (2016)Health Data Mining using Tensor Factorization: Methods and Applications.. Georgia Institute of Technology, Atlanta, GA, USA, (2020)base-search.net (ftgeorgiatech:oai:smartech.gatech.edu:1853/63498).SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping., , , , , , , and . KDD, page 2080-2089. ACM, (2018)Sparse Hierarchical Tucker Factorization and Its Application to Healthcare., , , and . ICDM, page 943-948. IEEE Computer Society, (2015)An input-adaptive and in-place approach to dense tensor-times-matrix multiply., , , , and . SC, page 76:1-76:12. ACM, (2015)