Most data-driven methods for fault diagnostics rely on the assumption of independently and
identically distributed data of training and testing. However, domain shift between the …

Real-world data often exhibits long tail distributions with heavy class imbalance, where the
majority classes can dominate the training process and alter the decision boundaries of the …

Machine learning models often perform poorly on subgroups that are underrepresented in
the training data. Yet, little is understood on the variation in mechanisms that cause …

Multimodal learning (MML) aims to jointly exploit the common priors of different modalities to
compensate for their inherent limitations. However, existing MML methods often optimize a …

We call on the Document AI (DocAI) community to re-evaluate current methodologies and
embrace the challenge of creating more practically-oriented benchmarks. Document …

From human physiology to environmental evolution, important processes in nature often
exhibit meaningful and strong periodic or quasi-periodic changes. Due to their inherent label …

We propose and address a new generalisation problem: can a model trained for action
recognition successfully classify actions when they are performed within a previously …

Deep regression models typically learn in an end-to-end fashion without explicitly
emphasizing a regression-aware representation. Consequently, the learned representations …

Long-term exposure to stress hurts human's mental and even physical health, and stress
monitoring is of increasing significance in the prevention, diagnosis, and management of …

Feature selection can be used to solve imbalanced classification problems encountered in
big data projects. There often exist multiple feature subsets achieving the same accuracy …