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What is Deep Learning Ising Model Insights about?

This paper investigates the effectiveness of deep neural networks compared to shallow networks in modeling the probability distribution of a two-dimensional Ising system near criticality. The authors find that the accuracy of generative models primarily depends on the number of neurons in the first hidden layer rather than the depth of the network, suggesting that shallow networks are more efficient for this specific task. The study highlights the implications for using generative neural networks in statist

Author

liyongxin0123

Language

EN