129 citations · 334 across the 6 of their papers we have counts for
7 papers
Monte Carlo Gradient Estimation in Machine Learning
Shakir Mohamed, Mihaela Rosca, Michael Figurnov +1
This paper is a broad and accessible survey of the methods we have at our disposal for Monte Carlo gradient estimation in machine learning and across the statistical sciences: the…
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…
Probabilistic Adaptive Computation Time
Michael Figurnov, Artem Sobolev, Dmitry Vetrov
We present a probabilistic model with discrete latent variables that control the computation time in deep learning models such as ResNets and LSTMs. A prior on the latent variables…
Spatially Adaptive Computation Time for Residual Networks
Michael Figurnov, Maxwell D. Collins, Yukun Zhu +4
This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image. This architecture is…