Fourier Stochastic Backpropagation

Amine Echraibi, Joachim Flocon Cholet, Stéphane Gosselin, Sandrine Vaton

Keywords: Stochastic Backpropagation, Variational Inference, Probabilistic Graphical Models, Deep Learning

Abstract: Backpropagating gradients through random variables is at the heart of numerous machine learning applications. In this paper, we present a general framework for deriving stochastic backpropagation rules for any distribution, discrete or continuous. Our approach exploits the link between the characteristic function and the Fourier transform, to transport the derivatives from the parameters of the distribution to the random variable. Our method generalizes previously known estimators, and results in new estimators for the gamma, beta, Dirichlet and Laplace distributions. Furthermore, we show that the classical deterministic backproapagation rule and the discrete random variable case, can also be interpreted through stochastic backpropagation.

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One-sentence Summary: Communicating new theoretical results concerning stochastic backpropagation.

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