Dataset Groups Activity Stream Alternating optimization method based on nonnegative matrix factorizations for deep neural networks The proposed method uses the MNIST and CIFAR10 datasets for fully-connected DNNs. BibTex: @dataset{Tetsuya_Sakurai_and_Akira_Imakura_and_Yuto_Inoue_and_Yasunori_Futamura_2024, abstract = {The proposed method uses the MNIST and CIFAR10 datasets for fully-connected DNNs.}, author = {Tetsuya Sakurai and Akira Imakura and Yuto Inoue and Yasunori Futamura}, doi = {10.57702/b7623jfz}, institution = {No Organization}, keyword = {'CIFAR10', 'Deep Neural Networks', 'MNIST'}, month = {dec}, publisher = {TIB}, title = {Alternating optimization method based on nonnegative matrix factorizations for deep neural networks}, url = {https://service.tib.eu/ldmservice/dataset/alternating-optimization-method-based-on-nonnegative-matrix-factorizations-for-deep-neural-networks}, year = {2024} }
