Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

Often we wish to transfer representational knowledge from one neural network to another.
Examples include distilling a large network into a smaller one, transferring knowledge from …

Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep
neural networks (DNNs) to execute complex inference tasks such as image classification …

Neural machine translation (NMT) offers a novel alternative formulation of translation that is
potentially simpler than statistical approaches. However to reach competitive performance …

Obtaining large, human labelled speech datasets to train models for emotion recognition is a
notoriously challenging task, hindered by annotation cost and label ambiguity. In this work …

Policies for complex visual tasks have been successfully learned with deep reinforcement
learning, using an approach called deep Q-networks (DQN), but relatively large (task …

Exponential surge in health care data, such as longitudinal data from electronic health
records (EHR), sensor data from intensive care unit (ICU), etc., is providing new …

Multi-exit architectures, in which a stack of processing layers is interleaved with early output
layers, allow the processing of a test example to stop early and thus save computation time …

Airborne hyperspectral remote sensing has the characteristics of high spatial and spectral
resolutions, and provides an opportunity for accurate and efficient inland water qauality …