Video URL
https://pirsa.org/16060005Machine Learning with Quantum-Inspired Tensor Networks
APA
(2016). Machine Learning with Quantum-Inspired Tensor Networks. Perimeter Institute for Theoretical Physics. https://pirsa.org/16060005
MLA
Machine Learning with Quantum-Inspired Tensor Networks. Perimeter Institute for Theoretical Physics, Jun. 14, 2016, https://pirsa.org/16060005
BibTex
@misc{ scivideos_PIRSA:16060005, doi = {10.48660/16060005}, url = {https://pirsa.org/16060005}, author = {}, keywords = {Quantum Matter}, language = {en}, title = {Machine Learning with Quantum-Inspired Tensor Networks}, publisher = {Perimeter Institute for Theoretical Physics}, year = {2016}, month = {jun}, note = {PIRSA:16060005 see, \url{https://scivideos.org/index.php/pirsa/16060005}} }
Abstract
Tensor networks have been very successful for approximating quantum states that would otherwise require exponentially many parameters.
I will discuss how a similar compression can be achieved in models used to machine learn data, such as sets of images, by representing the fitting parameters as a tensor network. The resulting model achieves state-of-the-art performance on standard classification tasks. I will discuss implications for machine learning research, exploring which insights from physics could be imported into this field.