104 citations · 367 across the 54 of their papers we have counts for
8 papers · 1 filter
EvoMO-SR: Multiobjective LLM-based Evolution of Symbolic Expressions with substructure guidance
Cristina Rossetti, Anna V. Kononova, Thomas Bäck +2
Symbolic Regression (SR) is a data-driven method for scientific discovery which searches for interpretable analytical relationships within data. Recently, Large Language Models (LL…
DQS: A Low-Budget Query Strategy for Enhancing Unsupervised Data-driven Anomaly Detection Approaches
Lucas Correia, Jan-Christoph Goos, Thomas Bäck +1
Truly unsupervised approaches for time series anomaly detection are rare in the literature. Those that exist suffer from a poorly set threshold, which hampers detection performance…
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…
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…
MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain Testing
Lucas Correia, Jan-Christoph Goos, Philipp Klein +2
A clear need for automatic anomaly detection applied to automotive testing has emerged as more and more attention is paid to the data recorded and manual evaluation by humans reach…
Deep Multiagent Reinforcement Learning: Challenges and Directions
Annie Wong, Thomas Bäck, Anna V. Kononova +1
This paper surveys the field of deep multiagent reinforcement learning. The combination of deep neural networks with reinforcement learning has gained increased traction in recent…