Learning the Ising Model with Generative Neural Networks

15 Jan 2020Francesco D'AngeloLucas Böttcher

Recent advances in deep learning and neural networks have led to an increased interest in the application of generative models in statistical and condensed matter physics. In particular, restricted Boltzmann machines (RBMs) and variational autoencoders (VAEs) as specific classes of neural networks have been successfully applied in the context of physical feature extraction and representation learning... (read more)

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