Medical coding is the task of assigning medical codes to clinical free-t...
Although supervised deep learning has revolutionized speech and audio
pr...
We present simple methods for out-of-distribution detection using a trai...
Unsupervised representation learning for speech processing has matured
g...
Stochastic latent variable models (LVMs) achieve state-of-the-art perfor...
Spoken language understanding (SLU) tasks are usually solved by first
tr...
The two most common paradigms for end-to-end speech recognition are
conn...
Deep generative models have shown themselves to be state-of-the-art dens...
Recent advances in self-supervised learning through contrastive training...
We address a challenging and practical task of labeling questions in spe...
With the introduction of the variational autoencoder (VAE), probabilisti...
End-to-end automatic speech recognition (ASR) commonly transcribes audio...
End-to-end neural network based approaches to audio modelling are genera...
Nontrivial connectivity has allowed the training of very deep networks b...
Deep generative models trained with large amounts of unlabelled data hav...
Deep generative models parameterized by neural networks have recently
ac...
Variational Autoencoders are powerful models for unsupervised learning.
...
Applying traditional collaborative filtering to digital publishing is
ch...