From Probabilistic Graphical Models to Generalized Tensor Networks for 
Supervised Learning

Glasser, Ivan; Pancotti, Nicola; Cirac, J. Ignacio

doi:10.1109/ACCESS.2020.2986279

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From Probabilistic Graphical Models to Generalized Tensor Networks for Supervised Learning

Glasser, I., Pancotti, N., & Cirac, J. I. (2020). From Probabilistic Graphical Models to Generalized Tensor Networks for Supervised Learning. IEEE Access, 8, 68169-68182. doi:10.1109/ACCESS.2020.2986279.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0006-5922-0 Version Permalink: https://hdl.handle.net/21.11116/0000-0008-DF47-E

Genre: Journal Article

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Creators:
Glasser, Ivan^{1, 2}, Author
Pancotti, Nicola^{1, 2, 3}, Author
Cirac, J. Ignacio^{1, 2}, Author

Affiliations:
1Theory, Max Planck Institute of Quantum Optics, Max Planck Society, ou_1445571
2MCQST - Munich Center for Quantum Science and Technology, External Organizations, ou_3330166
3IMPRS (International Max Planck Research School), Max Planck Institute of Quantum Optics, Max Planck Society, ou_3164443

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Language(s): eng - English

Dates: Submitted: 2019-12-12Accepted: 2020-03-06Published Online: 2020-04-07

Publication Status: Published online

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Rev. Type: Peer

Identifiers: DOI: 10.1109/ACCESS.2020.2986279
URI: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9058650
Other: 5926

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Title: IEEE Access

Source Genre: Journal

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Publ. Info: New York, NY : IEEE

Pages: - Volume / Issue: 8 Sequence Number: - Start / End Page: 68169 - 68182 Identifier: ISSN: 2169-3536
CoNE: https://pure.mpg.de/cone/journals/resource/2169-3536