Convolutional architectures have recently been shown to be competitive on
many sequence modelling tasks when compared to the de-facto standard of
recurrent neural networks (RNNs), while providing computational and modeling
advantages due to inherent parallelism. However, currently there remains a
performance gap to more expressive stochastic RNN variants, especially those
with several layers of dependent random variables. In this work, we propose
stochastic temporal convolutional networks (STCNs), a novel architecture that
combines the computational advantages of temporal convolutional networks (TCN)
with the representational power and robustness of stochastic latent spaces. In
particular, we propose a hierarchy of stochastic latent variables that captures
temporal dependencies at different time-scales. The architecture is modular and
flexible due to the decoupling of the deterministic and stochastic layers. We
show that the proposed architecture achieves state of the art log-likelihoods
across several tasks. Finally, the model is capable of predicting high-quality
synthetic samples over a long-range temporal horizon in modeling of handwritten
text.

本文提出了一种新的 STCN 体系结构，其将时间卷积网络（TCN）的计算优势与具有不同时间尺度的时间依赖性的随机潜在空间的表示能力和鲁棒性相结合，提高了多个任务的对数似然度，并且在手写文本建模中能够预测长时间跨度内的高质量合成样本。