Work

Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling

Year: 2017

Type: conference-paper

Abstract: Recurrent neural networks (RNNs) have shown promising performance for language modeling.However, traditional training of RNNs using back-propagation through time often suffers from overfitting.One reason for this is that stochastic optimization (used for large training sets) does not provide good estimates of model uncertainty.This paper leverages recent advances in stochastic gradient Markov Chain Monte Carlo (also appropriate for large training sets) to learn weight uncertainty in RNNs.It yields a principled Bayesian learning algorithm, adding gradient noise during training (enhancing exploration of the model-parameter space) and model averaging when testing.Extensive experiments on various RNN models and across a broad range of applications demonstrate the superiority of the proposed approach relative to stochastic optimization.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/p17-1030

Authors: Zhe Gan, Chunyuan Li, Changyou Chen, Yunchen Pu, Qinliang Su, Lawrence Carin

Institution: Duke University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid