Bayesian neural networks (BNN) and deep ensembles are principled approaches to
estimate the predictive uncertainty of a deep learning model. However their practicality in …

Accurate uncertainty quantification is a major challenge in deep learning, as neural
networks can make overconfident errors and assign high confidence predictions to out-of …

Interest in dialog systems has grown substantially in the past decade. By extension, so too
has interest in developing and improving intent classification and slot-filling models, which …

Amidst the domestic labor shortage and worldwide pandemic in recent years, there has
been an urgent need for a digital means that allows construction site workers, particularly …

Uncertainty estimation is a crucial aspect of deploying dependable deep learning models in
safety-critical systems. In this study, we introduce a novel and efficient method for …

The battlefield environment is changing rapidly, and fast and accurate identification of the
tactical intention of enemy targets is an important condition for gaining a decision-making …

The ubiquity of smartphones has led to an increase in on demand healthcare being
supplied. For example, people can share their illness-related experiences with others similar …

Task-oriented bots (or simply bots) enable humans to perform tasks in natural language. For
example, to book a restaurant or check the weather. Crowdsourcing has become a …

In this demonstration paper we showcase an extensible and reusable pipeline for automatic
paraphrase generation, ie, reformulating sentences using different words. Capturing the …

Writing utterances to train conversational agents can be a challenging and time-consuming
task, and usually requires substantial expertise, meaning that novices face a steep learning …