129 citations · 334 across the 6 of their papers we have counts for
3 papers · 1 filter
Variational Autoencoder with Arbitrary Conditioning
Oleg Ivanov, Michael Figurnov, Dmitry Vetrov
We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining fe…
Implicit Reparameterization Gradients
Michael Figurnov, Shakir Mohamed, Andriy Mnih
By providing a simple and efficient way of computing low-variance gradients of continuous random variables, the reparameterization trick has become the technique of choice for trai…
Tensor Train decomposition on TensorFlow (T3F)
Alexander Novikov, Pavel Izmailov, Valentin Khrulkov +2
Tensor Train decomposition is used across many branches of machine learning. We present T3F -- a library for Tensor Train decomposition based on TensorFlow. T3F supports GPU execut…