no code implementations • 25 Mar 2024 • Marcin Pietroń, Dominik Żurek, Kamil Faber, Roberto Corizzo
Anomaly detection tools and methods enable key analytical capabilities in modern cyberphysical and sensor-based systems.
no code implementations • 4 Mar 2024 • Marcin Pietroń, Dominik Żurek, Kamil Faber, Roberto Corizzo
First, pruning reduces the number of weights, while preventing catastrophic drops in accuracy by means of a fast search process that identifies high sparsity levels.
no code implementations • 14 Aug 2023 • Marcin Pietroń, Dominik Żurek, Kamil Faber, Roberto Corizzo
Most of them have many problems with learning in dynamic and complex environments.
no code implementations • 25 May 2023 • Marcin Pietron, Dominik Zurek, Kamil Faber, Roberto Corizzo
Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems.
1 code implementation • 16 Mar 2023 • Kamil Faber, Dominik Zurek, Marcin Pietron, Nathalie Japkowicz, Antonio Vergari, Roberto Corizzo
Continual learning (CL) is one of the most promising trends in recent machine learning research.
no code implementations • 14 Mar 2023 • Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Nathalie Japkowicz
Anomaly detection is of paramount importance in many real-world domains, characterized by evolving behavior.
no code implementations • 8 Dec 2022 • Indranil Sur, Zachary Daniels, Abrar Rahman, Kamil Faber, Gianmarco J. Gallardo, Tyler L. Hayes, Cameron E. Taylor, Mustafa Burak Gurbuz, James Smith, Sahana Joshi, Nathalie Japkowicz, Michael Baron, Zsolt Kira, Christopher Kanan, Roberto Corizzo, Ajay Divakaran, Michael Piacentino, Jesse Hostetler, Aswin Raghavan
In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system.
no code implementations • 18 Jan 2022 • Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Michael Baron, Nathalie Japkowicz
Detecting relevant changes in dynamic time series data in a timely manner is crucially important for many data analysis tasks in real-world settings.
no code implementations • 8 Aug 2021 • Kamil Faber, Dominik Żurek, Marcin Pietroń, Kamil Piętak
To our knowledge, this is the first approach in which an ensemble deep learning anomaly detection model is built in a fully automatic way using a neuroevolution strategy.