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Computer Science

D-Index
32
Citations
8403
World Ranking
12894
National Ranking
629

Overview

Michael Pfeiffer is affiliated with the Bosch Center for Artificial Intelligence in Germany. Their research spans multiple fields, primarily within Engineering and Computer Science, with a focus on Artificial Intelligence, Aerospace Engineering, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, and Cognitive Neuroscience.

The scientist has contributed to several main topics throughout their work, including:

  • Adversarial Robustness in Machine Learning
  • Advanced Neural Network Applications
  • Advanced SAR Imaging Techniques
  • Radar Systems and Signal Processing
  • Advanced Memory and Neural Computing
  • Neural Dynamics and Brain Function
  • Ferroelectric and Negative Capacitance Devices

Michael Pfeiffer has authored a number of recent papers, with works published in various reputable venues. Some of the notable publications include:

  • Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks, 2020, Frontiers in Neuroscience
  • Robust Anomaly Detection in Images Using Adversarial Autoencoders, 2020, Lecture Notes in Computer Science
  • Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning, 2020, arXiv (Cornell University)
  • A generative growth model for thalamocortical axonal branching in primary visual cortex, 2020, PLoS Computational Biology
  • Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing, 2022, 2022 IEEE Radar Conference (RadarConf22)

Frequent publication venues for Michael Pfeiffer's work include:

  • arXiv (Cornell University)
  • Frontiers in Neuroscience
  • Lecture Notes in Computer Science
  • 2022 IEEE Radar Conference (RadarConf22)
  • PLoS Computational Biology

The scientist often collaborates with a group of coauthors, with frequent collaborators being:

  • Kanil Patel
  • William Beluch
  • Kilian Rambach
  • Bin Yang
  • Alexander Kugele

Best Publications

  • Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing

    Peter U. Diehl;Daniel Neil;Jonathan Binas;Matthew Cook

  • Training Deep Spiking Neural Networks Using Backpropagation.

    Jun Haeng Lee;Tobi Delbruck;Michael Pfeiffer

  • Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.

    Bodo Rueckauer;Iulia-Alexandra Lungu;Yuhuang Hu;Michael Pfeiffer;Michael Pfeiffer

  • Gland segmentation in colon histology images: The GlaS challenge contest

    Korsuk Sirinukunwattana;Josien P.W. Pluim;Hao Chen;Xiaojuan Qi

  • Deep Learning With Spiking Neurons: Opportunities and Challenges.

    Michael Pfeiffer;Thomas Pfeil

  • Real-time classification and sensor fusion with a spiking deep belief network

    Peter O'Connor;Daniel Neil;Shih-Chii Liu;Tobi Delbruck

  • Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences

    Daniel Neil;Michael Pfeiffer;Shih-Chii Liu

  • Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

    Bernhard Nessler;Michael Pfeiffer;Michael Pfeiffer;Lars Buesing;Wolfgang Maass

  • DVS Benchmark Datasets for Object Tracking, Action Recognition, and Object Recognition.

    Yuhuang Hu;Hongjie Liu;Michael Pfeiffer;Tobi Delbruck

  • Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences

    Daniel Neil;Michael Pfeiffer;Shih-Chii Liu

  • STDP enables spiking neurons to detect hidden causes of their inputs

    Bernhard Nessler;Michael Pfeiffer;Wolfgang Maass

  • Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization

    Philipp Kainz;Philipp Kainz;Michael Pfeiffer;Martin Urschler

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

    Bodo Rueckauer;Iulia-Alexandra Lungu;Yuhuang Hu;Michael Pfeiffer

  • Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms

    Evangelos Stromatias;Daniel Neil;Michael Pfeiffer;Francesco Galluppi

  • Optimal sizing of a solar thermal building installation using particle swarm optimization

    Raffaele Bornatico;Michael Pfeiffer;Michael Pfeiffer;Andreas Witzig;Lino Guzzella

  • Deep Learning-based Object Classification on Automotive Radar Spectra

    Kanil Patel;Kilian Rambach;Tristan Visentin;Daniel Rusev

  • Scalable energy-efficient, low-latency implementations of trained spiking Deep Belief Networks on SpiNNaker

    Evangelos Stromatias;Daniel Neil;Francesco Galluppi;Michael Pfeiffer

  • Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks

    Alexander Kugele;Alexander Kugele;Thomas Pfeil;Michael Pfeiffer;Elisabetta Chicca

  • Robust Anomaly Detection in Images Using Adversarial Autoencoders

    Laura Beggel;Michael Pfeiffer;Bernd Bischl

  • Real-Time Gesture Interface Based on Event-Driven Processing From Stereo Silicon Retinas

    Jun Haeng Lee;Tobi Delbruck;Michael Pfeiffer;Paul K. J. Park

  • Learning to be efficient: algorithms for training low-latency, low-compute deep spiking neural networks

    Daniel Neil;Michael Pfeiffer;Shih-Chii Liu

  • A framework for plasticity implementation on the SpiNNaker neural architecture

    Francesco Galluppi;Xavier Lagorce;Evangelos Stromatias;Michael Pfeiffer

Frequent Co-Authors

Shih-Chii Liu
Shih-Chii Liu University of Zurich
Giacomo Indiveri
Giacomo Indiveri University of Zurich
Wolfgang Maass
Wolfgang Maass Graz University of Technology
Tobi Delbruck
Tobi Delbruck ETH Zurich
Bin Yang
Bin Yang University of Stuttgart
Martin Urschler
Martin Urschler University of Auckland
Steve Furber
Steve Furber University of Manchester
Bernd Bischl
Bernd Bischl Ludwig-Maximilians-Universität München

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