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 • 28 Dec 2021 • Marcin Pietroń, Dominik Żurek
One of the most common techniques for improving the efficiency of CNN models is weight pruning and quantization.
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.
no code implementations • 17 Jul 2020 • Dominik Żurek, Marcin Pietroń
This paper presents the impact of using quantization on the efficiency of multi-class text classification in the training process of a support vector machine (SVM).