Bayesian Recurrent Neural Networks
This work addresses the need for better uncertainty quantification and efficiency in RNNs for applications like language modeling and image captioning, though it is incremental as it builds on existing Bayesian neural network methods.
The authors tackled the problem of improving uncertainty estimation and regularization in Recurrent Neural Networks (RNNs) by proposing a variational Bayes scheme, achieving an 80% reduction in parameters and demonstrating superior performance on language modeling and image captioning tasks.
In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by 80\%. Secondly, we demonstrate how a novel kind of posterior approximation yields further improvements to the performance of Bayesian RNNs. We incorporate local gradient information into the approximate posterior to sharpen it around the current batch statistics. We show how this technique is not exclusive to recurrent neural networks and can be applied more widely to train Bayesian neural networks. We also empirically demonstrate how Bayesian RNNs are superior to traditional RNNs on a language modelling benchmark and an image captioning task, as well as showing how each of these methods improve our model over a variety of other schemes for training them. We also introduce a new benchmark for studying uncertainty for language models so future methods can be easily compared.