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Found 6 papers, 5 papers with code
From Tensor Network Quantum States to Tensorial Recurrent Neural Networks
We show that any matrix product state (MPS) can be exactly represented by a recurrent neural network (RNN) with a linear memory update.
NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems
We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics.
Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks
Efficient sampling of complex high-dimensional probability distributions is a central task in computational science.
RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior
In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG-Flow, which can separate information at different scales of images and extract disentangled representations at each scale.
Influenza Modeling Based on Massive Feature Engineering and International Flow Deconvolution
Finally, both the original model and the perturbed model are tested on regional examples, as validations of our models.
Solving Statistical Mechanics Using Variational Autoregressive Networks
We propose a general framework for solving statistical mechanics of systems with finite size.
