Michael Pfeiffer

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Universität Zürich ∙ ETH Zurich

Bosch Deep Learning Hardware Benchmark

The widespread use of Deep Learning (DL) applications in science and ind...
08/24/2020 ∙ by Armin Runge, et al. ∙ 0 ∙ share

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

Post-hoc calibration is a common approach for providing high-quality con...
06/23/2020 ∙ by Kanil Patel, et al. ∙ 0 ∙ share

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On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration

Uncertainty estimates help to identify ambiguous, novel, or anomalous in...
12/16/2019 ∙ by Kanil Patel, et al. ∙ 22 ∙ share

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Robust Anomaly Detection in Images using Adversarial Autoencoders

Reliably detecting anomalies in a given set of images is a task of high ...
01/18/2019 ∙ by Laura Beggel, et al. ∙ 0 ∙ share

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Data-driven Summarization of Scientific Articles

Data-driven approaches to sequence-to-sequence modelling have been succe...
04/24/2018 ∙ by Nikola I. Nikolov, et al. ∙ 0 ∙ share

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

Deep convolutional neural networks (CNNs) have shown great potential for...
12/13/2016 ∙ by Bodo Rueckauer, et al. ∙ 0 ∙ share

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Deep counter networks for asynchronous event-based processing

Despite their advantages in terms of computational resources, latency, a...
11/02/2016 ∙ by Jonathan Binas, et al. ∙ 0 ∙ share

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Prediction of Manipulation Actions

Looking at a person's hands one often can tell what the person is going ...
10/03/2016 ∙ by Cornelia Fermuller, et al. ∙ 0 ∙ share

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Training Deep Spiking Neural Networks using Backpropagation

Deep spiking neural networks (SNNs) hold great potential for improving t...
08/31/2016 ∙ by Jun Haeng Lee, et al. ∙ 0 ∙ share

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Precise deep neural network computation on imprecise low-power analog hardware

There is an urgent need for compact, fast, and power-efficient hardware ...
06/23/2016 ∙ by Jonathan Binas, et al. ∙ 0 ∙ share

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Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest

Colorectal adenocarcinoma originating in intestinal glandular structures...
03/01/2016 ∙ by Korsuk Sirinukunwattana, et al. ∙ 0 ∙ share

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Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers

Solving constraint satisfaction problems (CSPs) is a notoriously expensi...
11/02/2015 ∙ by Jonathan Binas, et al. ∙ 0 ∙ share

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Michael Pfeiffer

Featured Co-authors

Hao Chen

Yi Zhang

Pheng-Ann Heng

Bin Yang

Yezhou Yang

Bernd Bischl

Cornelia Fermuller

Xiaojuan Qi

Josien P. W. Pluim

Tobi Delbruck

Elia Bruni

Bosch Deep Learning Hardware Benchmark

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

On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration

Robust Anomaly Detection in Images using Adversarial Autoencoders

Data-driven Summarization of Scientific Articles

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

Deep counter networks for asynchronous event-based processing

Prediction of Manipulation Actions

Training Deep Spiking Neural Networks using Backpropagation

Precise deep neural network computation on imprecise low-power analog hardware

Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest

Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers