1 code implementation • 11 Jan 2024 • Dilyara Bareeva, Marina M. -C. Höhne, Alexander Warnecke, Lukas Pirch, Klaus-Robert Müller, Konrad Rieck, Kirill Bykov
Deep Neural Networks (DNNs) are capable of learning complex and versatile representations, however, the semantic nature of the learned concepts remains unknown.
no code implementations • 17 Apr 2023 • Alexander Warnecke, Julian Speith, Jan-Niklas Möller, Konrad Rieck, Christof Paar
In this paper, we challenge this assumption and introduce a backdoor attack that completely resides within a common hardware accelerator for machine learning.
1 code implementation • 26 Aug 2021 • Alexander Warnecke, Lukas Pirch, Christian Wressnegger, Konrad Rieck
In this paper, we propose the first method for unlearning features and labels.
no code implementations • 19 Oct 2020 • Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, Konrad Rieck
With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas.
no code implementations • 20 May 2020 • Yuliang Sun, Tai Fei, Xibo Li, Alexander Warnecke, Ernst Warsitz, Nils Pohl
In this paper, a real-time signal processing frame-work based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system to recognize gestures is proposed.
2 code implementations • 5 Jun 2019 • Alexander Warnecke, Daniel Arp, Christian Wressnegger, Konrad Rieck
Deep learning is increasingly used as a building block of security systems.