176 citations · 1.4k across the 70 of their papers we have counts for
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Out-of-distribution Neural Inference in Dynamical Ising Models
Yuan-Bin Zhu, Shuang Qiao, Shi-Ju Ran
Neural networks are increasingly used to infer hidden physical structure from dynamical observations, yet it remains unclear whether their out-of-distribution performance reflects…
Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters
Yong Qing, Ke Li, Peng-Fei Zhou +1
Neural network (NN) designed for challenging machine learning tasks is in general a highly nonlinear mapping that contains massive variational parameters. High complexity of NN, if…
Residual Matrix Product State for Machine Learning
Ye-Ming Meng, Jing Zhang, Peng Zhang +2
Tensor network, which originates from quantum physics, is emerging as an efficient tool for classical and quantum machine learning. Nevertheless, there still exists a considerable…
Tangent-Space Gradient Optimization of Tensor Network for Machine Learning
Zheng-zhi Sun, Shi-ju Ran, Gang Su
The gradient-based optimization method for deep machine learning models suffers from gradient vanishing and exploding problems, particularly when the computational graph becomes de…
Generative Tensor Network Classification Model for Supervised Machine Learning
Zheng-Zhi Sun, Cheng Peng, Ding Liu +2
Tensor network (TN) has recently triggered extensive interests in developing machine-learning models in quantum many-body Hilbert space. Here we purpose a generative TN classificat…