Michael Figurnov | DeepAI

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∙ 06/25/2019

Monte Carlo Gradient Estimation in Machine Learning

This paper is a broad and accessible survey of the methods we have at ou...

10 Shakir Mohamed, et al. ∙

∙ 06/06/2018

Universal Conditional Machine

We propose a single neural probabilistic model based on variational auto...

4 Oleg Ivanov, et al. ∙

∙ 05/22/2018

Implicit Reparameterization Gradients

By providing a simple and efficient way of computing low-variance gradie...

0 Michael Figurnov, et al. ∙

∙ 01/05/2018

Tensor Train decomposition on TensorFlow (T3F)

Tensor Train decomposition is used across many branches of machine learn...

0 Alexander Novikov, et al. ∙

∙ 12/01/2017

Probabilistic Adaptive Computation Time

We present a probabilistic model with discrete latent variables that con...

0 Michael Figurnov, et al. ∙

∙ 12/07/2016

Spatially Adaptive Computation Time for Residual Networks

This paper proposes a deep learning architecture based on Residual Netwo...

0 Michael Figurnov, et al. ∙

∙ 04/30/2015

PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions

We propose a novel approach to reduce the computational cost of evaluati...

0 Michael Figurnov, et al. ∙

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