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Tensorizing flows: a tool for variational inference
Fueled by the expressive power of deep neural networks, normalizing flows have achieved spectacular success in generative modeling, or learning to draw new samples from a distribution given a finite dataset of training samples.
High-dimensional density estimation with tensorizing flow
We propose the tensorizing flow method for estimating high-dimensional probability density functions from the observed data.
Autonomous learning of nonlocal stochastic neuron dynamics
When random excitations are modeled as Gaussian white noise, the joint PDF of neuron states satisfies exactly a Fokker-Planck equation.
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