Etienne Müller, M.Sc.
Technische Universität München
Postadresse
Postal:
Boltzmannstr. 3
85748 Garching b. München
Dienstort
Informatik 6 - Lehrstuhl für Robotik, Künstliche Intelligenz und Echtzeitsysteme (Prof. Knoll)
Work:
Boltzmannstr. 3(5607)/III
85748 Garching b. München
- Tel.: +49 (89) 289 - 18133
- Raum: 5607.03.037
- etienne.mueller@tum.de
Curriculum Vitae
Etienne Müller is a research assistant and PhD candidate since 2018. He received his Master's degree in Product Development and his Bachelor's Degree in Mechanical Engineering at the Hamburg University of Technology in 2017 and 2014, respectively.
Etienne's current research topic is the development of spiking neural networks in the context of path planning and decision making.
Research Interests
Conversion of today's commonly used analog neural network to spiking neural network for the usage in neuromorphic computing.
Thesis Supervision
Finished:
- Master Thesis (2021): Performance of Time to First Spike EncodedSpiking Neural Networks
- Guided Research (2021): Conversion of TransformerNets
- Master Thesis (2021): Conversion of LSTM-based RNN
- Master Thesis (2021): Conversion of GRU-based RNN
- Research Internship (2020): Carla as Open Source Platform for Analyzing and Evaluating Autonomous Driving
- Master Thesis (2020): Converting Analogue to Spiking Convolutional Neural Networks for Object Detection
- Graduation Thesis (2019): Semantic Segmentation of Integrated Circuit Layout Images
Publications
2021
- A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks. Neural Processing Letters, 2021 mehr… BibTeX Volltext ( DOI )
- 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
- End-to-end Spiking Neural Network for Speech Recognition Using Resonating Input Neurons. 30th International Conference on Artificial Neural Networks (ICANN), 2021accepted mehr… BibTeX
- Minimizing Inference Time: Optimization Methods for Converted Deep Spiking Neural Networks. International Joint Conference on Neural Networks (IJCNN), 2021accepted mehr… BibTeX
2020
- Hand Gesture Recognition using Hierarchical Temporal Memory on Radar Sequence Data. Bernstein Conference 2020, 2020 mehr… BibTeX Volltext ( DOI )
- Resonate-and-Fire Neurons as Frequency Selective Input Encoders for Spiking Neural Networks. 2020, mehr… BibTeX Volltext (mediaTUM)
- Faster Conversion of Analog to Spiking Neural Networks by Error Centering. Bernstein Conference 2020, 2020 mehr… BibTeX Volltext ( DOI )