Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

Given an unsupervised outlier detection task on a new dataset, how can we automatically
select a good outlier detection algorithm and its hyperparameter (s)(collectively called a …

Deep learning (DL) techniques have recently found success in anomaly detection (AD)
across various fields such as finance, medical services, and cloud computing. However …

We study embedding table placement for distributed recommender systems, which aims to
partition and place the tables on multiple hardware devices (eg, GPUs) to balance the …

Multivariate time series (MTS) data are becoming increasingly ubiquitous in networked
systems, eg, IoT systems and 5G networks. Anomaly detection in MTS refers to identifying …

Recent studies give more attention to the anomaly detection (AD) methods that can leverage
a handful of labeled anomalies along with abundant unlabeled data. These existing …

Sensors in cyber-physical systems often capture interconnected processes and thus emit
correlated time series (CTS), the forecasting of which enables important applications. The …

Imbalanced learning is a fundamental challenge in data mining, where there is a
disproportionate ratio of training samples in each class. Over-sampling is an effective …

Promising progress has been made toward learning efficient time series representations in
recent years, but the learned representations often lack interpretability and do not encode …