Machine Learning

An Introduction: Machine Learning vs Tensor Network


Related Works In This Field

 

Ours Works:

On the Equivalence of Restricted Boltzmann Machines and Tensor Network States

 

· Restricted Boltzmann machine (RBM) is one of the fundamental building blocks of deep learning. RBM finds wide applications in dimensional reduction, feature extraction, and recommender systems via modeling the probability distributions of a variety of input data including natural images, speech signals, and customer ratings, etc. We build a bridge between RBM and tensor network states (TNS) widely used in quantum many-body physics research. We devise efficient algorithms to translate an RBM into the commonly used TNS......

 

Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines

 

· We compare and contrast the statistical physics and quantum physics inspired approaches for unsupervised generative modeling of classical data. The two approaches represent probabilities of observed data using energy-based models and quantum states respectively.Classical and quantum information patterns of the target datasets therefore provide principled guidelines for structural design and learning in these two approaches. Taking the restricted Boltzmann machines (RBM) as an example, we analyze the information theoretical bounds of the two approaches......

Tao Xiang' s Group

HOME    |   MEMBERS    |    RESEARCH    |    PUBLICATIONS    |  LECTURE | WORKSHOP |  GET IN TOUCH

Copyright © 中国科学院物理研究所

添加微信好友,详细了解产品
使用企业微信
“扫一扫”加入群聊
复制成功
添加微信好友,详细了解产品
我知道了