Control Flow in Active Inference Systems—Part II: Tensor Networks as General Models of Control Flow-Reference-Cited by-同舟云学术

Control Flow in Active Inference Systems—Part II: Tensor Networks as General Models of Control Flow

Published:2023-06 Issue:2 Volume:9 Page:246-256
ISSN:2372-2061
Container-title:IEEE Transactions on Molecular, Biological and Multi-Scale Communications
language:
Short-container-title:IEEE Trans. Mol. Biol. Multi-Scale Commun.

Author:

Fields Chris¹^ORCID,Fabrocini Filippo²^ORCID,Friston Karl³^ORCID,Glazebrook James F.⁴^ORCID,Hazan Hananel¹^ORCID,Levin Michael¹^ORCID,Marcianò Antonino⁵^ORCID

Affiliation:

1. Allen Discovery Center, Tufts University, Medford, MA, USA

2. College of Design and Innovation, Tongji University, Shanghai, China

3. Wellcome Centre for Human Neuroimaging, University College London, London, U.K.

4. Department of Mathematics and Computer Science, Eastern Illinois University, Charleston, IL, USA

5. Center for Field Theory and Particle Physics and the Department of Physics, Fudan University, Shanghai, China

Funder

Wellcome Centre for Human Neuroimaging

Canada–U.K. Artificial Intelligence Initiative

European Union’s Horizon 2020 Framework Programme for Research and Innovation

Guy Foundation

John Templeton Foundation

Shanghai Municipality

Fudan University

Natural Science Foundation of China

Department of Physics, Fudan University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Modeling and Simulation,Bioengineering,Biotechnology

Link

http://xplorestaging.ieee.org/ielx7/6687308/10157988/10113744.pdf?arnumber=10113744

Reference109 articles.

1. Unsupervised generative omdeling using matrix product states;han;Phys Rev X,2018

2. From probabilistic graphical models to generalized tensor networks for supervised learning;glasser;arXiv 1806 05964,2018

3. Interaction decompositions for tensor network regression

4. Tensor networks for unsupervised machine learning;liu;arXiv 2106 12974,2021

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