151 citations · 191 across the 6 of their papers we have counts for
4 papers · 1 filter
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
Wei-Ning Hsu, Yu Zhang, James Glass
We present a factorized hierarchical variational autoencoder, which learns disentangled and interpretable representations from sequential data without supervision. Specifically, we…
Simple Recurrent Units for Highly Parallelizable Recurrence
Tao Lei, Yu Zhang, Sida I. Wang +2
Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SR…
Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation
Wei-Ning Hsu, Yu Zhang, James Glass
Domain mismatch between training and testing can lead to significant degradation in performance in many machine learning scenarios. Unfortunately, this is not a rare situation for…
Learning Latent Representations for Speech Generation and Transformation
Wei-Ning Hsu, Yu Zhang, James Glass
An ability to model a generative process and learn a latent representation for speech in an unsupervised fashion will be crucial to process vast quantities of unlabelled speech dat…