@conference {479,
	title = {Accuracy of a tensor network-based image classifier saturates with bond dimension},
	year = {2018},
	month = {18{\textendash}19 June 2018},
	pages = {P126},
	publisher = {ASTHRDP Graduate Scholars{\textquoteright} Conference},
	address = {PICC, Pasay City},
	abstract = {In the field of physics, tensor networks have been successfully applied to solve for problems requiring high-dimensionality. As data required in data science are also high-dimensional, tensor methods have also gained appreciation as a tool in machine learning. In this study, we have utilized an existing tensor network-based machine learning program to investigate on the role of bond dimension in the learning rate and the learning accuracy in an image classification problem. In our simulation, the increase in bond dimension leads to improved accuracy but easily reached a saturation point where the gains are overshadowed by the computational cost.},
	author = {Mykhal Gideoni L. Mangada and Francis N. C. Paraan}
}
@inproceedings {464,
	title = {Benchmarking a tensor network-based machine learning algorithm for supervised image classification},
	booktitle = {Proceedings of the 36th Samahang Pisika ng Pilipinas Physics Conference},
	year = {2018},
	month = {6{\textendash}9 June 2018},
	pages = {SPP-2018-PB-23},
	address = {Puerto Princesa City, Philippines},
	abstract = {Real-world data is often scarce and the careful setting of hyperparameters to efficiently train machine learning algorithms is necessary. In this study the role of bond dimension on the performance of an existing tensor network-based machine learning program was benchmarked on the MNIST and fashion-MNIST data sets. In particular, the effects of bond dimension on the accuracy and execution times of the algorithm were investigated. Results show that a bond dimension of less than 100 is sufficient to reach an acceptable classification accuracy, and increasing the bond dimension further extends the execution time and consumes prohibitive amounts of memory without providing a reasonable improvement in accuracy.},
	url = {https://paperview.spp-online.org/proceedings/article/view/SPP-2018-PB-23},
	author = {Mykhal Gideoni L. Mangada and Francis N. C. Paraan}
}
