中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Boltzmann machines as two-dimensional tensor networks

文献类型:期刊论文

作者Li, Sujie1; Pan, Feng1; Zhou, Pengfei1; Zhang, Pan2,3
刊名PHYSICAL REVIEW B
出版日期2021
卷号104期号:7页码:75154
ISSN号2469-9950
DOI10.1103/PhysRevB.104.075154
英文摘要Restricted Boltzmann machines (RBMs) and deep Boltzmann machines (DBMs) are important models in machine learning, and recently found numerous applications in quantum many-body physics. We show that there are fundamental connections between them and tensor networks. In particular, we demonstrate that any RBM and DBM can be exactly represented as a two-dimensional tensor network. This representation gives characterizations of the expressive power of RBMs and DBMs using entanglement structures of the tensor networks, and also provides an efficient tensor network contraction algorithm for the computing partition function of RBMs and DBMs. Using numerical experiments, we show that the proposed algorithm is more accurate than the state-of-the-art machine learning methods in estimating the partition function of RBMs and DBMs, and have potential applications in training DBMs for general machine learning tasks.
学科主题Materials Science ; Physics
语种英语
源URL[http://ir.itp.ac.cn/handle/311006/27278]  
专题理论物理研究所_理论物理所1978-2010年知识产出
作者单位1.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Phys Sci, Beijing 100049, Peoples R China
3.UCAS, Hangzhou Inst Adv Study, Sch Fundamental Phys & Math Sci, Hangzhou 310024, Peoples R China
4.Int Ctr Theoret Phys Asia Pacific, Beijing, Peoples R China
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Li, Sujie,Pan, Feng,Zhou, Pengfei,et al. Boltzmann machines as two-dimensional tensor networks[J]. PHYSICAL REVIEW B,2021,104(7):75154.
APA Li, Sujie,Pan, Feng,Zhou, Pengfei,&Zhang, Pan.(2021).Boltzmann machines as two-dimensional tensor networks.PHYSICAL REVIEW B,104(7),75154.
MLA Li, Sujie,et al."Boltzmann machines as two-dimensional tensor networks".PHYSICAL REVIEW B 104.7(2021):75154.

入库方式: OAI收割

来源:理论物理研究所

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