58 citations · 83 across the 9 of their papers we have counts for
11 papers
Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation
Johanna Rock, Wolfgang Roth, Mate Toth +2
Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous vehicles. With a rising number of radar sensors and the so far unregulated a…
End-to-end Keyword Spotting using Neural Architecture Search and Quantization
David Peter, Wolfgang Roth, Franz Pernkopf
This paper introduces neural architecture search (NAS) for the automatic discovery of end-to-end keyword spotting (KWS) models in limited resource environments. We employ a differe…
Resource-efficient DNNs for Keyword Spotting using Neural Architecture Search and Quantization
David Peter, Wolfgang Roth, Franz Pernkopf
This paper introduces neural architecture search (NAS) for the automatic discovery of small models for keyword spotting (KWS) in limited resource environments. We employ a differen…
Quantized Neural Networks for Radar Interference Mitigation
Johanna Rock, Wolfgang Roth, Paul Meissner +1
Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous vehicles. Key performance factors are weather resistance and the possibility…
On Resource-Efficient Bayesian Network Classifiers and Deep Neural Networks
Wolfgang Roth, Günther Schindler, Holger Fröning +1
We present two methods to reduce the complexity of Bayesian network (BN) classifiers. First, we introduce quantization-aware training using the straight-through gradient estimator…
Differentiable TAN Structure Learning for Bayesian Network Classifiers
Wolfgang Roth, Franz Pernkopf
Learning the structure of Bayesian networks is a difficult combinatorial optimization problem. In this paper, we consider learning of tree-augmented naive Bayes (TAN) structures fo…
Resource-Efficient Speech Mask Estimation for Multi-Channel Speech Enhancement
Lukas Pfeifenberger, Matthias Zöhrer, Günther Schindler +3
While machine learning techniques are traditionally resource intensive, we are currently witnessing an increased interest in hardware and energy efficient approaches. This need for…
Resource-Efficient Neural Networks for Embedded Systems
Wolfgang Roth, Günther Schindler, Bernhard Klein +5
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-e…
Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning (Full Version)
Bernhard K. Aichernig, Roderick Bloem, Masoud Ebrahimi +6
Models play an essential role in the design process of cyber-physical systems. They form the basis for simulation and analysis and help in identifying design problems as early as p…
Parameterized Structured Pruning for Deep Neural Networks
Guenther Schindler, Wolfgang Roth, Franz Pernkopf +1
As a result of the growing size of Deep Neural Networks (DNNs), the gap to hardware capabilities in terms of memory and compute increases. To effectively compress DNNs, quantizatio…
Efficient and Robust Machine Learning for Real-World Systems
Franz Pernkopf, Wolfgang Roth, Matthias Zoehrer +7
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource ef…