We propose a model for hierarchical structured data as an extension to the stochastic temporal convolutional network. The proposed model combines an autoregressive model with a hierarchical variational autoencoder and downsampling to achieve superior computational complexity. We evaluate the proposed model on two different types of sequential data: speech and handwritten text. The results are promising with the proposed model achieving state-of-the-art performance.
翻译:我们提出一个等级结构数据模型,作为分层时间变异网络的延伸。拟议模型将自动递减模型与等级变异自动编码器和下抽样相结合,以实现较高的计算复杂性。我们评估了两种不同类型相继数据的拟议模型:语音和手写文字。拟议模型取得最新性能,其结果很有希望。