@article {376,
	title = {Simple techniques for improving deep neural network outcomes on commodity hardware},
	journal = {AIP Conf. Proc. (8th Jagna International Workshop)},
	volume = {1871},
	year = {2017},
	pages = {040001 },
	abstract = {We benchmark improvements in the performance of deep neural networks (DNN) on the MNIST data test upon implementing two simple modifications to the algorithm that have little overhead computational cost. First is GPU parallelization on a commodity graphics card, and second is initializing the DNN with random orthogonal weight matrices prior to optimization. Eigenspectra analysis of the weight matrices reveal that the initially orthogonal matrices remain nearly orthogonal after training. The probability distributions from which these orthogonal matrices are drawn are also shown to significantly affect the performance of these deep neural networks.},
	doi = {10.1063/1.4996523},
	url = {http://aip.scitation.org/doi/10.1063/1.4996523},
	author = {Nicholas Christopher A Colina and Carlos E Perez and Francis N. C. Paraan}
}
@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}
}