The study of artificial intelligence (AI) has been a continuous endeavor of scientists and
engineers for over 65 years. The simple contention is that human-created machines can do …

The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

We present a novel framework for multivariate time series representation learning based on
the transformer encoder architecture. The framework includes an unsupervised pre-training …

Time series forecasting has been a widely explored task of great importance in many
applications. However, it is common that real-world time series data are recorded in a short …

The detection of anomalies in time series has gained ample academic and industrial
attention. However, no comprehensive benchmark exists to evaluate time-series anomaly …

Learning useful representations with little or no supervision is a key challenge in artificial
intelligence. We provide an in-depth review of recent advances in representation learning …

Intelligent agents should be able to learn useful representations by observing changes in
their environment. We model such observations as pairs of non-iid images sharing at least …

Time series constitute a challenging data type for machine learning algorithms, due to their
highly variable lengths and sparse labeling in practice. In this paper, we tackle this …

Multivariate time series with missing values are common in areas such as healthcare and
finance, and have grown in number and complexity over the years. This raises the question …

While the choice of prior is one of the most critical parts of the Bayesian inference workflow,
recent Bayesian deep learning models have often fallen back on vague priors, such as …