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ORCIDMichael Pfeiffer 0001
Person information
- affiliation: Bosch Center for Artificial Intelligence, Renningen, Germany
- affiliation (former): ETH Zurich, Switzerland
- affiliation (former): University of Zurich, Institute of Neuroinformatics, Switzerland
- affiliation (former): Graz University of Technology, Austria
Other persons with the same name
- Michael Pfeiffer 0002 — RWTH Aachen University, Germany
- Michael Pfeiffer 0003 — Bielefeld University, Faculty of Technology, Germany
- Michael Pfeiffer 0004 — University of Linz, Institute of Practical Computer Science, Austria
- Michael Pfeiffer 0005 — Software Competence Center Hagenberg GmbH, Austria
- Michael Pfeiffer 0006 — Technische Universität Ilmenau, Germany
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2020 – today
- 2023
[c19]Alexander Kugele, Thomas Pfeil, Michael Pfeiffer, Elisabetta Chicca:
How Many Events Make an Object? Improving Single-frame Object Detection on the 1 Mpx Dataset. CVPR Workshops 2023: 3913-3922- 2021
- [c18]Alexander Kugele
, Thomas Pfeil, Michael Pfeiffer, Elisabetta Chicca:
Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision. GCPR 2021: 297-312 - [c17]Kanil Patel, William H. Beluch, Bin Yang, Michael Pfeiffer, Dan Zhang:
Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning. ICLR 2021 - [i18]Kanil Patel, William Beluch, Kilian Rambach, Adriana-Eliza Cozma, Michael Pfeiffer, Bin Yang:
Investigation of Uncertainty of Deep Learning-based Object Classification on Radar Spectra. CoRR abs/2106.05870 (2021) - [i17]Kanil Patel, William Beluch, Kilian Rambach, Michael Pfeiffer, Bin Yang:
Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing. CoRR abs/2109.12851 (2021) - [i16]Alexander Kugele, Thomas Pfeil, Michael Pfeiffer, Elisabetta Chicca:
Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision. CoRR abs/2112.03423 (2021) - [i15]Robert Haas, Michael Pfeiffer:
Brain-inspired Computing - Introduction to the Special Theme. ERCIM News 2021(125) (2021) - 2020
- [j9]Pegah Kassraian-Fard, Michael Pfeiffer, Roman Bauer:
A generative growth model for thalamocortical axonal branching in primary visual cortex. PLoS Comput. Biol. 16(2) (2020) - [c16]Kanil Patel, William Beluch, Dan Zhang, Michael Pfeiffer, Bin Yang:
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration. ICPR 2020: 8029-8036 - [i14]Kanil Patel, William Beluch, Bin Yang, Michael Pfeiffer, Dan Zhang:
Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning. CoRR abs/2006.13092 (2020) - [i13]Armin Runge, Thomas Wenzel, Dimitrios Bariamis, Benedikt Sebastian Staffler, Lucas Rêgo Drumond, Michael Pfeiffer:
Bosch Deep Learning Hardware Benchmark. CoRR abs/2008.10293 (2020)
2010 – 2019
- 2019
- [j8]Laura Beggel, Bernhard X. Kausler, Martin Schiegg, Michael Pfeiffer, Bernd Bischl:
Time series anomaly detection based on shapelet learning. Comput. Stat. 34(3): 945-976 (2019) - [c15]Laura Beggel, Michael Pfeiffer, Bernd Bischl:
Robust Anomaly Detection in Images Using Adversarial Autoencoders. ECML/PKDD (1) 2019: 206-222 - [i12]Laura Beggel, Michael Pfeiffer, Bernd Bischl:
Robust Anomaly Detection in Images using Adversarial Autoencoders. CoRR abs/1901.06355 (2019) - [i11]Kanil Patel, William Beluch, Dan Zhang, Michael Pfeiffer, Bin Yang:
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration. CoRR abs/1912.07458 (2019) - 2018
- [j7]Cornelia Fermüller, Fang Wang, Yezhou Yang, Konstantinos Zampogiannis, Yi Zhang, Francisco Barranco, Michael Pfeiffer:
Prediction of Manipulation Actions. Int. J. Comput. Vis. 126(2-4): 358-374 (2018) - [i10]Nikola I. Nikolov, Michael Pfeiffer, Richard H. R. Hahnloser:
Data-driven Summarization of Scientific Articles. CoRR abs/1804.08875 (2018) - 2017
- [j6]Korsuk Sirinukunwattana, Josien P. W. Pluim, Hao Chen, Xiaojuan Qi, Pheng-Ann Heng, Yun Bo Guo, Li Yang Wang, Bogdan J. Matuszewski, Elia Bruni, Urko Sanchez, Anton Böhm, Olaf Ronneberger, Bassem Ben Cheikh, Daniel Racoceanu, Philipp Kainz, Michael Pfeiffer, Martin Urschler, David R. J. Snead, Nasir M. Rajpoot:
Gland segmentation in colon histology images: The glas challenge contest. Medical Image Anal. 35: 489-502 (2017) - 2016
- [c14]Jonathan Binas, Giacomo Indiveri, Michael Pfeiffer:
Spiking analog VLSI neuron assemblies as constraint satisfaction problem solvers. ISCAS 2016: 2094-2097 - [c13]Dora Sumislawska, Qiao Ning, Michael Pfeiffer, Giacomo Indiveri:
Wide dynamic range weights and biologically realistic synaptic dynamics for spike-based learning circuits. ISCAS 2016: 2491-2494 - [c12]Daniel Neil, Michael Pfeiffer, Shih-Chii Liu:
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences. NIPS 2016: 3882-3890 - [c11]Daniel Neil, Michael Pfeiffer, Shih-Chii Liu:
Learning to be efficient: algorithms for training low-latency, low-compute deep spiking neural networks. SAC 2016: 293-298 - [i9]Korsuk Sirinukunwattana, Josien P. W. Pluim, Hao Chen, Xiaojuan Qi, Pheng-Ann Heng, Yun Bo Guo, Li Yang Wang, Bogdan J. Matuszewski, Elia Bruni, Urko Sanchez, Anton Böhm, Olaf Ronneberger, Bassem Ben Cheikh, Daniel Racoceanu, Philipp Kainz, Michael Pfeiffer, Martin Urschler, David R. J. Snead, Nasir M. Rajpoot:
Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest. CoRR abs/1603.00275 (2016) - [i8]Jonathan Binas, Daniel Neil, Giacomo Indiveri, Shih-Chii Liu, Michael Pfeiffer:
Precise deep neural network computation on imprecise low-power analog hardware. CoRR abs/1606.07786 (2016) - [i7]Junhaeng Lee, Tobi Delbrück, Michael Pfeiffer:
Training Deep Spiking Neural Networks using Backpropagation. CoRR abs/1608.08782 (2016) - [i6]Cornelia Fermüller, Fang Wang, Yezhou Yang, Konstantinos Zampogiannis, Yi Zhang, Francisco Barranco, Michael Pfeiffer:
Prediction of Manipulation Actions. CoRR abs/1610.00759 (2016) - [i5]Daniel Neil, Michael Pfeiffer, Shih-Chii Liu:
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences. CoRR abs/1610.09513 (2016) - [i4]Jonathan Binas, Giacomo Indiveri, Michael Pfeiffer:
Deep counter networks for asynchronous event-based processing. CoRR abs/1611.00710 (2016) - [i3]Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, Michael Pfeiffer:
Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks. CoRR abs/1612.04052 (2016) - 2015
- [c10]Jonathan Binas, Giacomo Indiveri, Michael Pfeiffer:
Local structure helps learning optimized automata in recurrent neural networks. IJCNN 2015: 1-7 - [c9]Peter U. Diehl, Daniel Neil, Jonathan Binas, Matthew Cook, Shih-Chii Liu, Michael Pfeiffer:
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing. IJCNN 2015: 1-8 - [c8]Evangelos Stromatias, Daniel Neil, Francesco Galluppi, Michael Pfeiffer, Shih-Chii Liu, Steve B. Furber:
Scalable energy-efficient, low-latency implementations of trained spiking Deep Belief Networks on SpiNNaker. IJCNN 2015: 1-8 - [c7]Evangelos Stromatias, Daniel Neil, Francesco Galluppi, Michael Pfeiffer, Shih-Chii Liu, Steve B. Furber:
Live demonstration: Handwritten digit recognition using spiking deep belief networks on SpiNNaker. ISCAS 2015: 1901 - [c6]Tobi Delbrück, Michael Pfeiffer, R. Juston, Garrick Orchard, Elias Mueggler, Alejandro Linares-Barranco, M. W. Tilden:
Human vs. computer slot car racing using an event and frame-based DAVIS vision sensor. ISCAS 2015: 2409-2412 - [i2]Jonathan Binas, Giacomo Indiveri, Michael Pfeiffer:
Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers. CoRR abs/1511.00540 (2015) - [i1]Philipp Kainz, Michael Pfeiffer, Martin Urschler:
Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation. CoRR abs/1511.06919 (2015) - 2014
- [j5]Jonathan Binas, Ueli Rutishauser, Giacomo Indiveri, Michael Pfeiffer:
Learning and stabilization of winner-take-all dynamics through interacting excitatory and inhibitory plasticity. Frontiers Comput. Neurosci. 8: 68 (2014) - [j4]Roman Bauer, Frédéric Zubler, Sabina S. Pfister, Andreas Hauri, Michael Pfeiffer, Dylan R. Muir, Rodney J. Douglas:
Developmental Self-Construction and -Configuration of Functional Neocortical Neuronal Networks. PLoS Comput. Biol. 10(12) (2014) - [j3]Junhaeng Lee, Tobi Delbrück, Michael Pfeiffer, Paul K. J. Park, Chang-Woo Shin, Hyunsurk Ryu, Byung-Chang Kang:
Real-Time Gesture Interface Based on Event-Driven Processing From Stereo Silicon Retinas. IEEE Trans. Neural Networks Learn. Syst. 25(12): 2250-2263 (2014) - 2013
- [j2]Bernhard Nessler, Michael Pfeiffer, Lars Buesing, Wolfgang Maass:
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity. PLoS Comput. Biol. 9(4) (2013) - [c5]Sadique Sheik, Michael Pfeiffer, Fabio Stefanini, Giacomo Indiveri:
Spatio-temporal Spike Pattern Classification in Neuromorphic Systems. Living Machines 2013: 262-273 - 2012
- [c4]Junhaeng Lee, Tobi Delbrück, Paul K. J. Park, Michael Pfeiffer, Chang-Woo Shin, Hyunsurk Ryu, Byung-Chang Kang:
Live demonstration: Gesture-based remote control using stereo pair of dynamic vision sensors. ISCAS 2012: 741-745 - 2010
- [j1]Michael Pfeiffer, Bernhard Nessler, Rodney J. Douglas, Wolfgang Maass:
Reward-Modulated Hebbian Learning of Decision Making. Neural Comput. 22(6): 1399-1444 (2010)
2000 – 2009
- 2009
- [c3]Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass:
STDP enables spiking neurons to detect hidden causes of their inputs. NIPS 2009: 1357-1365 - 2008
- [c2]Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass:
Hebbian Learning of Bayes Optimal Decisions. NIPS 2008: 1169-1176 - 2007
- [c1]Gerhard Neumann, Michael Pfeiffer, Wolfgang Maass:
Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs. ECML 2007: 250-261
Coauthor Index
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