Machine Learning
An Introduction: Machine Learning vs Tensor Network
Machine Learning
An Introduction: Machine Learning vs Tensor Network（链接）
Related Works In This Field（链接）
Ours Works:
On the Equivalence of Restricted Boltzmann Machines and Tensor Network States（链接）
· 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（链接）
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
