Daniel Auge, M.Sc.

Daniel Auge, M.Sc.

Technische Universität München

Postadresse

Postal:
Boltzmannstr. 3
85748 Garching b. München

Raum: 5607.03.037
daniel.auge@tum.de

Curriculum Vitae

(2018-now) Ph.D. candidate, Technical University of Munich
(2018) Software Developer, Ibeo Automotive Systems GmbH
(2015-2017) Master of Science in Electrical Engineering, Hamburg University of Technology
(2011-2015) Bachelor of Science in General Engineering Science, Hamburg University of Technology

Research Interests

Development of spiking neural networks for the monitoring of analog radar sensor front ends.

Thesis Supervision

Ongoing:

Finished:

Master thesis: Winner-take-all Circuits in Spiking Neural Networks for Object Tracking in Event-based Video Data
Master thesis: Development of a Network Architecture for Speech Recognition Using Spiking Networks
Master thesis: Embedded AI - Development of a Toolchain for the Adaption of Neural Networks to the Requirements of Embedded Systems
Bachelor thesis: Developing an Annotation Tool for Machine Learning
Bachelor thesis: Radar Gesture Recognition with Hierarchical Temporal Memory

Publications

2022

Hille, Julian; Auge, Daniel; Grassmann, Cyprian; Knoll, Alois: FMCW radar2radar Interference Detection with a Recurrent Neural Network. 2022 IEEE Radar Conference (RadarConf22), IEEE, 2022 mehr… BibTeX Volltext ( DOI ) Volltext (mediaTUM)
Mueller, Etienne; Auge, Daniel; Klimaschka, Simon; Knoll, Alois: Neural Oscillations for Energy-Efficient Hardware Implementation of Sparsely Activated Deep Spiking Neural Networks. Association for the Advancement of Artificial Intelligence (AAAI), 2022Practical Deep Learning in the Wild mehr… BibTeX Volltext (mediaTUM)
Mueller, Etienne; Auge, Daniel; Knoll, Alois: Exploiting Inhomogeneities of Subthreshold Transistors as Populations of Spiking Neurons. International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2022 mehr… BibTeX

2021

Auge, Daniel; Hille, Julian; Mueller, Etienne; Knoll, Alois: A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks. Neural Processing Letters, 2021 mehr… BibTeX Volltext ( DOI ) Volltext (mediaTUM)
Daniel Auge, Julian Hille, Etienne Mueller, Alois Knoll: Hand Gesture Recognition in Range-Doppler Images Using Binary Activated Spiking Neural Networks. IEEE International Conference on Automatic Face and Gesture Recognition 2021, 2021accepted mehr… BibTeX Volltext (mediaTUM)
Daniel Auge, Julian Hille, Felix Kreutz, Etienne Mueller, Alois Knoll: End-to-end Spiking Neural Network for Speech Recognition Using Resonating Input Neurons. 30th International Conference on Artificial Neural Networks (ICANN), 2021accepted mehr… BibTeX Volltext (mediaTUM)
Etienne Mueller, Daniel Auge, Alois Knoll: Normalization Hyperparameter Search for Converted Spiking Neural Networks. Bernstein Conference, 2021 mehr… BibTeX Volltext ( DOI ) Volltext (mediaTUM)
Etienne Mueller, Julius Hansjakob, Daniel Auge, Alois Knoll: Minimizing Inference Time: Optimization Methods for Converted Deep Spiking Neural Networks. International Joint Conference on Neural Networks (IJCNN), 2021accepted mehr… BibTeX Volltext (mediaTUM)
Mueller, Etienne; Studenyak, Viktor; Auge, Daniel; Knoll, Alois: Spiking Transformer Networks: A Rate Coded Approach for Processing Sequential Data. Internation Conference on Systems and Informatics (ICSAI), 2021accepted mehr… BibTeX Volltext (mediaTUM)
Nair, Saasha; Shafaei, Sina; Auge, Daniel; Knoll, Alois: An Evaluation of “Crash Prediction Networks” (CPN) for Autonomous Driving Scenarios in CARLA Simulator. SafeAI 2021 - The AAAI's Workshop on Artificial Intelligence Safety, 2021, mehr… BibTeX Volltext (mediaTUM)

2020

Auge D, Wenner P, Mueller E: Hand Gesture Recognition using Hierarchical Temporal Memory on Radar Sequence Data. Bernstein Conference 2020, 2020 mehr… BibTeX Volltext ( DOI )
Daniel Auge, Etienne Mueller: Resonate-and-Fire Neurons as Frequency Selective Input Encoders for Spiking Neural Networks. 2020, mehr… BibTeX Volltext (mediaTUM)
Mueller E, Hansjakob J, Auge D: Faster Conversion of Analog to Spiking Neural Networks by Error Centering. Bernstein Conference 2020, 2020 mehr… BibTeX Volltext ( DOI )