Etienne Müller, M.Sc.
Technische Universität München
85748 Garching b. München
Informatik 6 - Lehrstuhl für Robotik, Künstliche Intelligenz und Echtzeitsysteme (Prof. Knoll)
85748 Garching b. München
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.
Conversion of today's commonly used analog neural network to spiking neural network for the usage in neuromorphic computing.
- 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
- A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks. Neural Processing Letters, 2021 more… BibTeX Full text ( 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 more… BibTeX
- End-to-end Spiking Neural Network for Speech Recognition Using Resonating Input Neurons. 30th International Conference on Artificial Neural Networks (ICANN), 2021accepted more… BibTeX
- Normalization Hyperparameter Search for Converted Spiking Neural Networks. Bernstein Conference, 2021 more… BibTeX Full text ( DOI )
- Minimizing Inference Time: Optimization Methods for Converted Deep Spiking Neural Networks. International Joint Conference on Neural Networks (IJCNN), 2021accepted more… BibTeX
- Spiking Transformer Networks: A Rate Coded Approach for Processing Sequential Data. Internation Conference on Systems and Informatics (ICSAI), 2021accepted more… BibTeX
- Hand Gesture Recognition using Hierarchical Temporal Memory on Radar Sequence Data. Bernstein Conference 2020, 2020 more… BibTeX Full text ( DOI )
- Resonate-and-Fire Neurons as Frequency Selective Input Encoders for Spiking Neural Networks. 2020, more… BibTeX Full text (mediaTUM)
- Faster Conversion of Analog to Spiking Neural Networks by Error Centering. Bernstein Conference 2020, 2020 more… BibTeX Full text ( DOI )