@article{koroko:hal-04266143,
TITLE = {Efficient approximations of the fisher matrix in neural networks using kronecker product singular value decomposition},
AUTHOR = {Koroko, Abdoulaye and Anciaux-Sedrakian, Ani and Gharbia, Ibtihel and Gar{\`e}s, Val{\'e}rie and Haddou, Mounir and Tran, Quang Huy},
URL = {https://hal.science/hal-04266143},
JOURNAL = {ESAIM: Proceedings and Surveys},
PUBLISHER = {EDP Sciences},
VOLUME = {73},
PAGES = {218-237},
YEAR = {2023},
DOI = {10.1051/proc/202373218},
KEYWORDS = {Singular value decomposition ; Kronecker product ; Singular value ; Kronecker delta ; Artificial neural network ; Mathematics ; Matrix decomposition ; Algorithm ; Gradient descent ; Matrix (chemical analysis) ; Applied mathematics ; Approximations of $\pi$ ; Product (mathematics) ; Block (permutation group theory) ; Diagonal ; Computer science ; Mathematical optimization ; Artificial intelligence ; Combinatorics ; Materials science ; Composite material ; Eigenvalues and eigenvectors ; Physics ; Geometry ; Quantum mechanics},
PDF = {https://hal.science/hal-04266143/file/proc2307311.pdf},
HAL_ID = {hal-04266143},
HAL_VERSION = {v1},
}