40 citations · 58 across the 26 of their papers we have counts for
5 papers · 2 filters
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series
Lucas Correia, Jan-Christoph Goos, Thomas Bäck +1
Benchmarking anomaly detection approaches for multivariate time series is a challenging task due to a lack of high-quality datasets. Current publicly available datasets are too sma…
Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions
Lucas Correia, Jan-Christoph Goos, Philipp Klein +2
Time-series anomaly detection plays an important role in engineering processes, like development, manufacturing and other operations involving dynamic systems. These processes can…
TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data
Lucas Correia, Jan-Christoph Goos, Philipp Klein +2
As attention to recorded data grows in the realm of automotive testing and manual evaluation reaches its limits, there is a growing need for automatic online anomaly detection. Thi…
Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring
Christian Internò, Elena Raponi, Markus Olhofer +5
The practical deployment of Federated Learning (FL) on resource-constrained devices is fundamentally limited by the high cost of training large models and the instability caused by…
Solving Deep Reinforcement Learning Tasks with Evolution Strategies and Linear Policy Networks
Annie Wong, Jacob de Nobel, Thomas Bäck +2
Although deep reinforcement learning methods can learn effective policies for challenging problems such as Atari games and robotics tasks, algorithms are complex, and training time…