Networks of Networks: Complexity Class Principles Applied to Compound AI Systems Design

Autor: Davis, Jared Quincy, Hanin, Boris, Chen, Lingjiao, Bailis, Peter, Stoica, Ion, Zaharia, Matei

As practitioners seek to surpass the current reliability and quality frontier of monolithic models, Compound AI Systems consisting of many language model inference calls are increasingly employed. In this work, we construct systems, which we call Net

Externí odkaz: http://arxiv.org/abs/2407.16831

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Quantum transport in graphene nanoribbon networks: complexity reduction by a network decimation algorithm

Autor: Rodemund, Tom Simon, Teichert, Fabian, Hentschel, Martina, Schuster, Jörg

Publikováno v: New Journal of Physics 25 (2023) 013001

We study electronic quantum transport in graphene nanoribbon (GNR) networks on mesoscopic length scales. We focus on zigzag GNRs and investigate the conductance properties of statistical networks. To this end we use a density-functional-based tight-b

Externí odkaz: http://arxiv.org/abs/2212.07238

Enhanced rock weathering boosts ecosystem multifunctionality via improving microbial networks complexity in a tropical forest plantation.

Autor: Wang X; Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China., Li G; CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China. Electronic address: ligc@iae.ac.cn., Ali A; College of Life Sciences, Hebei University, Baoding, China., Algora C; Laboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS) CSIC, Sevilla, E-41012, Spain., Baquerizo MD; Laboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS) CSIC, Sevilla, E-41012, Spain., Goll DS; Laboratoire Des Sciences du Climat et de l'Environnement, Université Paris-Saclay, Paris, France., Vicca S; Plants and Ecosystems (PLECO), Biology Department, University of Antwerp, Wilrijk, Belgium., Xu T; Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China., Bi B; Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China., Chen Q; Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China., Lin L; CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, China., Fang Y; CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China., Hao Z; Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China., Li Z; Key Laboratory of Biodiversity and Environment on the Qinghai-Tibetan Plateau, Ministry of Education, School of Ecology and Environment, Tibet University, Lhasa, China., Yuan Z; Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China; Key Laboratory of Biodiversity and Environment on the Qinghai-Tibetan Plateau, Ministry of Education, School of Ecology and Environment, Tibet University, Lhasa, China. Electronic address: zqyuan@nwpu.edu.cn.

Publikováno v: Journal of environmental management [J Environ Manage] 2024 Nov 29; Vol. 373, pp. 123477. Date of Electronic Publication: 2024 Nov 29.

Learning with tree tensor networks: complexity estimates and model selection

Autor: Michel, Bertrand, Nouy, Anthony

Publikováno v: Bernoulli 28(2): 910-936 (May 2022)

Tree tensor networks, or tree-based tensor formats, are prominent model classes for the approximation of high-dimensional functions in computational and data science. They correspond to sum-product neural networks with a sparse connectivity associate

Externí odkaz: http://arxiv.org/abs/2007.01165