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Asynchronous Stochastic Variational Inference.

Mohamed, S., Bouchachia, A. and Sayed-Mouchaweh, M., 2019. Asynchronous Stochastic Variational Inference. In: INNS Big Data and Deep Learning 2019: Proceedings of the International Neural Networks Society Conference, 16--18 April 2019, Genoa, Italy.

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Official URL: https://innsbddl2019.org/

Abstract

Stochastic variational inference (SVI) employs stochastic optimization to scale up Bayesian computation to massive data. Since SVI is at its core a stochastic gradient-based algorithm, horizontal parallelism can be harnessed to allow larger scale inference. We propose a lock-free parallel implementation for SVI which allows distributed computations over multiple slaves in an asynchronous style. We show that our implementation leads to linear speed-up while guaranteeing an asymptotic ergodic convergence rate O(1/ √ T) while the number of slaves is bounded by √ T (T is the total number of iterations). The implementation is done in a high-performance computing environment using message passing interface for python (MPI4py). The empirical evaluation shows that our parallel SVI is lossless, performing comparably well to its counterpart serial SVI with linear speed-up.

Item Type:Conference or Workshop Item (Paper)
Group:Faculty of Science & Technology
ID Code:32444
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:27 Jun 2019 14:17
Last Modified:03 Apr 2020 01:08

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