426 lines
16 KiB
BibTeX
426 lines
16 KiB
BibTeX
@article{KoldaBader2009,
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author = {Kolda, Tamara G. and Bader, Brett W.},
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title = {Tensor Decompositions and Applications},
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journal = {SIAM Review},
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volume = {51},
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number = {3},
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pages = {455-500},
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year = {2009},
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doi = {10.1137/07070111X},
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URL = {
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https://doi.org/10.1137/07070111X},
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eprint ={https://doi.org/10.1137/07070111X},
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abstract = { This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or N-way array. Decompositions of higher-order tensors (i.e., N-way arrays with \$N \geq 3\$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors. }
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}
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@article{RegMatrixReg-ZhouLi2014,
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author = {Zhou, Hua and Li, Lexin},
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title = {Regularized matrix regression},
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journal = {Journal of the Royal Statistical Society. Series B (Statistical Methodology)},
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volume = {76},
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number = {2},
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pages = {463--483},
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year = {2014},
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publisher = {[Royal Statistical Society, Wiley]}
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}
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@inproceedings{Nesterov1983,
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title = {A method of solving a convex programming problem with convergence rate $O(1/k^2)$},
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author = {Nesterov, Yurii Evgen'evich},
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booktitle = {Doklady Akademii Nauk},
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volume = {269},
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number = {3},
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pages = {543--547},
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year = {1983},
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organization= {Russian Academy of Sciences}
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}
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@book{StatInf-CasellaBerger2002,
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title = {{Statistical Inference}},
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author = {Casella, George and Berger, Roger L.},
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isbn = {0-534-24312-6},
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series = {Duxbury Advanced Series},
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year = {2002},
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edition = {2},
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publisher = {Thomson Learning}
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}
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@book{MatrixDiffCalc-MagnusNeudecker1999,
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title = {Matrix Differential Calculus with Applications in Statistics and Econometrics (Revised Edition)},
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author = {Magnus, Jan R. and Neudecker, Heinz},
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year = {1999},
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publisher = {John Wiley \& Sons Ltd},
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isbn = {0-471-98632-1}
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}
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@article{SymMatandJacobians-MagnusNeudecker1986,
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title = {Symmetry, 0-1 Matrices and Jacobians: A Review},
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author = {Magnus, Jan R. and Neudecker, Heinz},
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ISSN = {02664666, 14694360},
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URL = {http://www.jstor.org/stable/3532421},
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journal = {Econometric Theory},
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number = {2},
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pages = {157--190},
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publisher = {Cambridge University Press},
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urldate = {2023-10-03},
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volume = {2},
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year = {1986}
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}
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@book{MatrixAlgebra-AbadirMagnus2005,
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title = {Matrix Algebra},
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author = {Abadir, Karim M. and Magnus, Jan R.},
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year = {2005},
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publisher = {Cambridge University Press},
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series = {Econometric Exercises},
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collection = {Econometric Exercises},
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place = {Cambridge},
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doi = {10.1017/CBO9780511810800}
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}
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@article{TensorDecomp-HuLeeWang2022,
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author = {Hu, Jiaxin and Lee, Chanwoo and Wang, Miaoyan},
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title = {Generalized Tensor Decomposition With Features on Multiple Modes},
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journal = {Journal of Computational and Graphical Statistics},
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volume = {31},
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number = {1},
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pages = {204-218},
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year = {2022},
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publisher = {Taylor \& Francis},
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doi = {10.1080/10618600.2021.1978471},
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}
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@article{CovarEstSparseKron-LengPan2018,
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author = {Leng, Chenlei and Pan, Guangming},
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title = {{Covariance estimation via sparse Kronecker structures}},
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volume = {24},
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journal = {Bernoulli},
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number = {4B},
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publisher = {Bernoulli Society for Mathematical Statistics and Probability},
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pages = {3833 -- 3863},
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year = {2018},
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doi = {10.3150/17-BEJ980}
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}
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@article{sdr-PfeifferKaplaBura2021,
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author = {Pfeiffer, Ruth and Kapla, Daniel and Bura, Efstathia},
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title = {{Least squares and maximum likelihood estimation of sufficient reductions in regressions with matrix-valued predictors}},
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volume = {11},
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year = {2021},
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journal = {International Journal of Data Science and Analytics},
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doi = {10.1007/s41060-020-00228-y}
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}
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@article{sdr-BuraDuarteForzani2016,
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author = {Bura, Efstathia and Duarte, Sabrina and Forzani, Liliana},
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title = {Sufficient Reductions in Regressions With Exponential Family Inverse Predictors},
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journal = {Journal of the American Statistical Association},
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volume = {111},
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number = {515},
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pages = {1313-1329},
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year = {2016},
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publisher = {Taylor \& Francis},
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doi = {10.1080/01621459.2015.1093944}
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}
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@article{FisherLectures-Cook2007,
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author = {Cook, R. Dennis},
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journal = {Statistical Science},
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month = {02},
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number = {1},
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pages = {1--26},
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publisher = {The Institute of Mathematical Statistics},
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title = {{Fisher Lecture: Dimension Reduction in Regression}},
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volume = {22},
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year = {2007},
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doi = {10.1214/088342306000000682}
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}
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@article{asymptoticMLE-BuraEtAl2018,
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author = {Bura, Efstathia and Duarte, Sabrina and Forzani, Liliana and E. Smucler and M. Sued},
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title = {Asymptotic theory for maximum likelihood estimates in reduced-rank multivariate generalized linear models},
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journal = {Statistics},
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volume = {52},
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number = {5},
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pages = {1005-1024},
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year = {2018},
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publisher = {Taylor \& Francis},
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doi = {10.1080/02331888.2018.1467420},
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}
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@article{tsir-DingCook2015,
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author = {Shanshan Ding and R. Dennis Cook},
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title = {Tensor sliced inverse regression},
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journal = {Journal of Multivariate Analysis},
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volume = {133},
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pages = {216-231},
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year = {2015},
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issn = {0047-259X},
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doi = {10.1016/j.jmva.2014.08.015}
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}
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@article{lsir-PfeifferForzaniBura,
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author = {Pfeiffer, Ruth and Forzani, Liliana and Bura, Efstathia},
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year = {2012},
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month = {09},
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pages = {2414-27},
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title = {Sufficient dimension reduction for longitudinally measured predictors},
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volume = {31},
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journal = {Statistics in medicine},
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doi = {10.1002/sim.4437}
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}
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@Inbook{ApproxKron-VanLoanPitsianis1993,
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author = {Van Loan, C. F. and Pitsianis, N.},
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editor = {Moonen, Marc S. and Golub, Gene H. and De Moor, Bart L. R.},
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title = {Approximation with Kronecker Products},
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bookTitle = {Linear Algebra for Large Scale and Real-Time Applications},
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year = {1993},
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publisher = {Springer Netherlands},
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address = {Dordrecht},
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pages = {293--314},
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isbn = {978-94-015-8196-7},
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doi = {10.1007/978-94-015-8196-7_17}
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}
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@book{asymStats-van_der_Vaart1998,
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title = {Asymptotic Statistics},
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author = {{van der Vaart}, A.W.},
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series = {Asymptotic Statistics},
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year = {1998},
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publisher = {Cambridge University Press},
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series = {Cambridge Series in Statistical and Probabilistic Mathematics},
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isbn = {0-521-49603-9}
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}
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@book{measureTheory-Kusolitsch2011,
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title = {{M}a\ss{}- und {W}ahrscheinlichkeitstheorie},
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subtitle = {{E}ine {E}inf{\"u}hrung},
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author = {Kusolitsch, Norbert},
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series = {Springer-Lehrbuch},
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year = {2011},
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publisher = {Springer Vienna},
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isbn = {978-3-7091-0684-6},
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doi = {10.1007/978-3-7091-0685-3}
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}
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@book{optimMatrixMani-AbsilEtAl2007,
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title = {{Optimization Algorithms on Matrix Manifolds}},
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author = {Absil, P.-A. and Mahony, R. and Sepulchre, R.},
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year = {2007},
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publisher = {Princeton University Press},
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isbn = {9780691132983},
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note = {Full Online Text \url{https://press.princeton.edu/absil}}
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}
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@Inbook{geomMethodsOnLowRankMat-Uschmajew2020,
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author = {Uschmajew, Andr{\'e} and Vandereycken, Bart},
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editor = {Grohs, Philipp and Holler, Martin and Weinmann, Andreas},
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title = {Geometric Methods on Low-Rank Matrix and Tensor Manifolds},
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bookTitle = {Handbook of Variational Methods for Nonlinear Geometric Data},
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year = {2020},
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publisher = {Springer International Publishing},
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address = {Cham},
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pages = {261--313},
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isbn = {978-3-030-31351-7},
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doi = {10.1007/978-3-030-31351-7_9}
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}
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@book{introToSmoothMani-Lee2012,
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title = {Introduction to Smooth Manifolds},
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author = {Lee, John M.},
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year = {2012},
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journal = {Graduate Texts in Mathematics},
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publisher = {Springer New York},
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doi = {10.1007/978-1-4419-9982-5}
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}
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@book{introToRiemannianMani-Lee2018,
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title = {Introduction to Riemannian Manifolds},
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author = {Lee, John M.},
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year = {2018},
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journal = {Graduate Texts in Mathematics},
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publisher = {Springer International Publishing},
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doi = {10.1007/978-3-319-91755-9}
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}
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@misc{MLEonManifolds-HajriEtAl2017,
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title = {Maximum Likelihood Estimators on Manifolds},
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author = {Hajri, Hatem and Said, Salem and Berthoumieu, Yannick},
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year = {2017},
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journal = {Lecture Notes in Computer Science},
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publisher = {Springer International Publishing},
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pages = {692-700},
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doi = {10.1007/978-3-319-68445-1_80}
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}
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@article{relativity-Einstain1916,
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author = {Einstein, Albert},
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title = {Die Grundlage der allgemeinen Relativitätstheorie},
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year = {1916},
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journal = {Annalen der Physik},
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volume = {354},
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number = {7},
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pages = {769-822},
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doi = {10.1002/andp.19163540702}
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}
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@article{MultilinearOperators-Kolda2006,
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title = {Multilinear operators for higher-order decompositions.},
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author = {Kolda, Tamara Gibson},
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doi = {10.2172/923081},
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url = {https://www.osti.gov/biblio/923081},
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place = {United States},
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year = {2006},
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month = {4},
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type = {Technical Report}
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}
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@book{aufbauAnalysis-kaltenbaeck2021,
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title = {Aufbau Analysis},
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author = {Kaltenb\"ack, Michael},
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isbn = {978-3-88538-127-3},
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series = {Berliner Studienreihe zur Mathematik},
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edition = {27},
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year = {2021},
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publisher = {Heldermann Verlag}
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}
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@article{TensorNormalMLE-ManceurDutilleul2013,
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title = {Maximum likelihood estimation for the tensor normal distribution: Algorithm, minimum sample size, and empirical bias and dispersion},
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author = {Ameur M. Manceur and Pierre Dutilleul},
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journal = {Journal of Computational and Applied Mathematics},
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volume = {239},
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pages = {37-49},
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year = {2013},
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issn = {0377-0427},
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doi = {10.1016/j.cam.2012.09.017},
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url = {https://www.sciencedirect.com/science/article/pii/S0377042712003810}
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}
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@article{StatIdentTensorGaussian-DeesMandic2019,
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title = {A Statistically Identifiable Model for Tensor-Valued Gaussian Random Variables},
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author = {Bruno Scalzo Dees and Danilo P. Mandic},
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journal = {ArXiv},
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year = {2019},
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volume = {abs/1911.02915},
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url = {https://api.semanticscholar.org/CorpusID:207847615}
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}
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@article{Ising-Ising1924,
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author = {Ising, Ernst},
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title = {{Beitrag zur Theorie des Ferromagnetismus}},
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journal = {Zeitschrift f\"ur Physik},
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pages = {253-258},
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volume = {31},
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number = {1},
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year = {1924},
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month = {2},
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issn = {0044-3328},
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doi = {10.1007/BF02980577}
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}
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# TODO: Fix the following!!!
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@book{GraphicalModels-Whittaker2009,
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author = {J. Whittaker},
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title = {Graphical Models in Applied Multivariate Statistics},
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publisher = {Wiley},
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year = {2009}
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}
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@article{MVB-Dai2012,
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author = {B. Dai},
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title = {Multivariate bernoulli distribution models},
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year = {2012}
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}
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@article{MVB-DaiDingWahba2013,
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author = {B. Dai, S. Ding, and G. Wahba},
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title = {Multivariate bernoulli distribution},
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year = {2013}
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}
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@article{sdr-mixedPredictors-BuraForzaniEtAl2022,
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author = {Bura and Forzani and TODO},
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title = {Sufficient reductions in regression with mixed predictors},
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journal = {},
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volume = {},
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number = {},
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year = {2022}
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}
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@article{sparseIsing-ChengEtAt2014,
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author = {J. Cheng, E. Levina, and J. Wang, P.and Zhu},
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title = {A sparse Ising model with covariates},
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journal = {},
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volume = {},
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number = {},
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year = {2014},
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doi = {10.1111/biom.12202}
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}
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@book{deeplearningbook-GoodfellowEtAl2016,
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title = {Deep Learning},
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author = {Ian Goodfellow and Yoshua Bengio and Aaron Courville},
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publisher = {MIT Press},
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url = {\url{http://www.deeplearningbook.org}},
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year = {2016}
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}
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@misc{rmsprop-Hinton2012,
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title = {Neural networks for machine learning},
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author = {Hinton, G.},
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year = {2012}
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}
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@article{self-kapla2019,
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title = {Comparison of Different Word Embeddings and Neural Network Types for Sentiment Analysis of German Political Speeches},
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author = {Kapla, Daniel},
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year = {2019}
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}
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@article{MGCCA-GirkaEtAl2024,
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title = {Tensor generalized canonical correlation analysis},
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author = {Fabien Girka and Arnaud Gloaguen and Laurent {Le Brusquet} and Violetta Zujovic and Arthur Tenenhaus},
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year = {2024},
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journal = {Information Fusion},
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volume = {102},
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issn = {1566-2535},
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doi = {10.1016/j.inffus.2023.102045}
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}
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@Article{Rdimtools,
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title = {{Rdimtools}: An {R} Package for Dimension Reduction and Intrinsic Dimension Estimation},
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author = {Kisung You and Dennis Shung},
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journal = {Software Impacts},
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year = {2022},
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volume = {14},
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issn = {26659638},
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pages = {100414},
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doi = {10.1016/j.simpa.2022.100414},
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}
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@misc{lichess-database,
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author = {{Thibault Duplessis}},
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title = {lichess.org open database},
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year = {2013},
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url = {https://database.lichess.org},
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note = {visited on December 8, 2023},
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}
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@misc{stockfish,
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title = {Stockfish},
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year = {since 2008},
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author = {{The Stockfish developers (see \href{https://github.com/official-stockfish/Stockfish/blob/master/AUTHORS}{AUTHORS} file)}},
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url = {https://stockfishchess.org/},
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abstract = {Stockfish is a free and strong UCI chess engine.},
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}
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@mish{schachhoernchen,
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title = {Schach H\"ornchen},
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year = {development since 2021, first release pending},
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author = {Kapla, Daniel},
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url = {todo!}
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}
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