@inproceedings {313,
	title = {Importance of initialization of weight matrices in deep learning neural networks},
	booktitle = {Proceedings of the 34th Samahang Pisika ng Pilipinas Physics Congress},
	year = {2016},
	month = {18{\textendash}21 Aug 2016},
	pages = {SPP-2016-PA-21},
	address = {University of the Philippines Visayas, Iloilo City},
	abstract = {The success of deep neural networks relies on optimized weight matrices are initialized in different ways. This work reports learning improvement in a six-layer deep neural network that is initialized with orthogonal weight matrices when compared to other commonly-used initialization schemes. An analysis of the eigenvalue spectra of the optimized solutions implies that the space of orthogonal weight matrices lies close to the manifold of learned states.},
	author = {Nicholas Christopher A Colina and Carlos E Perez and Francis N. C. Paraan}
}