Unsupervised Generative Modeling Using Matrix Product States

Generative modeling, which learns joint probability distribution from data and generates samples according to it, is an important task in machine learning and artificial intelligence. Inspired by probabilistic interpretation of quantum physics, we propose a generative model using matrix product stat...

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Veröffentlicht in:	Physical review. X Jg. 8; H. 3; S. 031012
Hauptverfasser:	Han, Zhao-Yu, Wang, Jun, Fan, Heng, Wang, Lei, Zhang, Pan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	College Park American Physical Society 01.07.2018
Schlagworte:	Algorithms Allocations Artificial intelligence Datasets Entangled states Generative adversarial networks Handwriting Machine learning Mathematical models Modelling Parameters Probability distribution Probability theory Quantum computing Quantum phenomena Quantum physics Quantum theory Representations Samples Sampling Standard data Statistical analysis Statistical methods Tensors
ISSN:	2160-3308, 2160-3308
Online-Zugang:	Volltext
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