Many efforts have been devoted to training generative latent variable models with autoregressive decoders, such as recurrent neural networks (RNN). Stochastic recurrent models have been successful in capturing the variability observed in natural sequential data such as speech. We unify successful ideas from recently proposed architectures into a stochastic recurrent model: each step in the sequence is associated with a latent variable that is used to condition the recurrent dynamics for future steps. Training is performed with amortised variational inference where the approximate posterior is augmented with a RNN that runs backward through the sequence. In addition to maximizing the variational lower bound, we ease training of the latent variables by adding an auxiliary cost which forces them to reconstruct the state of the backward recurrent network. This provides the latent variables with a task-independent objective that enhances the performance of the overall model. We found this strategy to perform better than alternative approaches such as KL annealing. Although being conceptually simple, our model achieves state-of-the-art results on standard speech benchmarks such as TIMIT and Blizzard and competitive performance on sequential MNIST. Finally, we apply our model to language modeling on the IMDB dataset where the auxiliary cost helps in learning interpretable latent variables.

Recurrent Neural Networks (RNNs) are effective tools for processing sequential data.

In partially observable (PO) environments, how to infer unseen information from raw observations puzzled the agents.

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Such "permutation invariant" functions have been studied extensively recently. Here we argue that temporal architectures such as RNNs are highly relevant for such problems, despite the inherent dependence of RNNs on order.

A.1 Datasets Details of the datasets we introduce are presented in this section. Specific details about generation as well as statistics from the resulting datasets are delineated for each one below. A.1.1 Prefix sum data Binary string inputs of length n are generated by selecting a random integer in [0, 2 Datasets are produced by repeating this random process 10,000 times without replacement. Because the number of possible points increases exponentially as a function of n and the size of the generated dataset is fixed, it is important to note that the dataset becomes sparser in its ambient hypercube as n increases. Moreover, we are limited to datasets with binary strings of length n>13 to avoid duplicate data points.