Semi-supervised Anomaly Detection in Supercomputers

The third talk will focus on the frameworks that are able to leverage a limited number of labeled and a large amount of unlabeled data. The main motivation behind these frameworks is that supervised ML-based frameworks require large labeled data sets, and this requirement is restrictive for many real-world application domains. We will discuss two techniques in detail. The first technique requires only non-anomalous telemetry data to detect performance anomalies on compute nodes [5]. The second technique takes advantage of a few labeled anomalous samples to classify anomaly types [3]. We finally provide a glimpse towards the opportunities stemming from the combination of supervised and semi-supervised approaches [6].