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Michael Pfeiffer

Bosch Center for Artificial Intelligence, Renningen, Germany

Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision


Dec 06, 2021
Alexander Kugele, Thomas Pfeil, Michael Pfeiffer, Elisabetta Chicca

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* Accepted at DAGM German Conference on Pattern Recognition (GCPR 2021) 

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Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing


Sep 27, 2021
Kanil Patel, William Beluch, Kilian Rambach, Michael Pfeiffer, Bin Yang

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* Submitted to IEEE Radar Conference 2022 

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Investigation of Uncertainty of Deep Learning-based Object Classification on Radar Spectra


Jun 01, 2021
Kanil Patel, William Beluch, Kilian Rambach, Adriana-Eliza Cozma, Michael Pfeiffer, Bin Yang

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* IEEE Radar Conference 2021 
* 6 pages 

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Bosch Deep Learning Hardware Benchmark


Aug 24, 2020
Armin Runge, Thomas Wenzel, Dimitrios Bariamis, Benedikt Sebastian Staffler, Lucas Rego Drumond, Michael Pfeiffer

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* Presented in MLBench: Workshop on Benchmarking Machine Learning Workloads (https://sites.google.com/g.harvard.edu/mlbench/home

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Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning


Jun 23, 2020
Kanil Patel, William Beluch, Bin Yang, Michael Pfeiffer, Dan Zhang

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On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration


Dec 16, 2019
Kanil Patel, William Beluch, Dan Zhang, Michael Pfeiffer, Bin Yang

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Robust Anomaly Detection in Images using Adversarial Autoencoders


Jan 18, 2019
Laura Beggel, Michael Pfeiffer, Bernd Bischl

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Data-driven Summarization of Scientific Articles


Apr 24, 2018
Nikola I. Nikolov, Michael Pfeiffer, Richard H. R. Hahnloser

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* 8 pages, 3 figures. 7th International Workshop on Mining Scientific Publications, LREC 2018 

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Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation


Oct 10, 2017
Philipp Kainz, Michael Pfeiffer, Martin Urschler

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* An extended version of this work has been published in PeerJ (https://doi.org/10.7717/peerj.3874), so please cite our journal version instead of this preprint 

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Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks


Dec 13, 2016
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, Michael Pfeiffer

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* 9 pages, 2 figures, presented at the workshop "Computing with Spikes" at NIPS 2016, Barcelona, Spain 

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