54 citations · 56 across the 9 of their papers we have counts for
5 papers · 1 filter
FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks
Laines Schmalwasser, Niklas Penzel, Joachim Denzler +1
Concepts such as objects, patterns, and shapes are how humans understand the world. Building on this intuition, concept-based explainability methods aim to study representations le…
Anomalous Agreement: How to find the Ideal Number of Anomaly Classes in Correlated, Multivariate Time Series Data
Ferdinand Rewicki, Joachim Denzler, Julia Niebling
Detecting and classifying abnormal system states is critical for condition monitoring, but supervised methods often fall short due to the rarity of anomalies and the lack of labele…
Functional Tensor Decompositions for Physics-Informed Neural Networks
Sai Karthikeya Vemuri, Tim Büchner, Julia Niebling +1
Physics-Informed Neural Networks (PINNs) have shown continuous and increasing promise in approximating partial differential equations (PDEs), although they remain constrained by th…
Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry
Ferdinand Rewicki, Jakob Gawlikowski, Julia Niebling +1
The detection of abnormal or critical system states is essential in condition monitoring. While much attention is given to promptly identifying anomalies, a retrospective analysis…
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
Ferdinand Rewicki, Joachim Denzler, Julia Niebling
Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare, and cybersecurity. While the abundance of available methods ma…