The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

Many modern unsupervised or semi-supervised machine learning algorithms rely on
Bayesian probabilistic models. These models are usually intractable and thus require …

We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …

The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

In this overview paper, data-driven learning model-based cooperative localization and
location data processing are considered, in line with the emerging machine learning and big …

Gaussian process classification is a popular method with a number of appealing properties.
We show how to scale the model within a variational inducing point framework, out …

Deep kernel learning combines the non-parametric flexibility of kernel methods with the
inductive biases of deep learning architectures. We propose a novel deep kernel learning …

Abstract To scale Gaussian processes (GPs) to large data sets we introduce the robust
Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts model for …

Variational autoencoders (VAE) are a powerful and widely-used class of models to learn
complex data distributions in an unsupervised fashion. One important limitation of VAEs is …

Accurate forecasts of wind gusts are crucially important for wind power generation, severe
weather warnings, and the regulation of vehicle speed. To improve the short-term and long …