Search Results for author: Michael Pfeiffer

Found 19 papers, 3 papers with code

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

no code implementations6 Dec 2021 Alexander Kugele, Thomas Pfeil, Michael Pfeiffer, Elisabetta Chicca

In this article we propose a hybrid architecture for end-to-end training of deep neural networks for event-based pattern recognition and object detection, combining a spiking neural network (SNN) backbone for efficient event-based feature extraction, and a subsequent analog neural network (ANN) head to solve synchronous classification and detection tasks.

Event-based vision object-detection +2

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

no code implementations27 Sep 2021 Kanil Patel, William Beluch, Kilian Rambach, Michael Pfeiffer, Bin Yang

The focus of this article is to learn deep radar spectra classifiers which offer robust real-time uncertainty estimates using label smoothing during training.

Decision Making

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

no code implementations1 Jun 2021 Kanil Patel, William Beluch, Kilian Rambach, Adriana-Eliza Cozma, Michael Pfeiffer, Bin Yang

Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar. In addition to high accuracy, it is crucial for decision making in autonomous vehicles to evaluate the reliability of the predictions; however, decisions of DL networks are non-transparent.

Autonomous Vehicles Decision Making +2

Bosch Deep Learning Hardware Benchmark

no code implementations24 Aug 2020 Armin Runge, Thomas Wenzel, Dimitrios Bariamis, Benedikt Sebastian Staffler, Lucas Rego Drumond, Michael Pfeiffer

The widespread use of Deep Learning (DL) applications in science and industry has created a large demand for efficient inference systems.

Autonomous Driving

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

1 code implementation ICLR 2021 Kanil Patel, William Beluch, Bin Yang, Michael Pfeiffer, Dan Zhang

The goal of this paper is to resolve the identified issues of HB in order to provide calibrated confidence estimates using only a small holdout calibration dataset for bin optimization while preserving multi-class ranking accuracy.


On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration

no code implementations16 Dec 2019 Kanil Patel, William Beluch, Dan Zhang, Michael Pfeiffer, Bin Yang

Uncertainty estimates help to identify ambiguous, novel, or anomalous inputs, but the reliable quantification of uncertainty has proven to be challenging for modern deep networks.

Adversarial Attack Data Augmentation

Robust Anomaly Detection in Images using Adversarial Autoencoders

no code implementations18 Jan 2019 Laura Beggel, Michael Pfeiffer, Bernd Bischl

Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis.

Anomaly Detection

Data-driven Summarization of Scientific Articles

3 code implementations24 Apr 2018 Nikola I. Nikolov, Michael Pfeiffer, Richard H. R. Hahnloser

Data-driven approaches to sequence-to-sequence modelling have been successfully applied to short text summarization of news articles.

Sentence Summarization

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

no code implementations13 Dec 2016 Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, Michael Pfeiffer

Deep convolutional neural networks (CNNs) have shown great potential for numerous real-world machine learning applications, but performing inference in large CNNs in real-time remains a challenge.

Deep counter networks for asynchronous event-based processing

no code implementations2 Nov 2016 Jonathan Binas, Giacomo Indiveri, Michael Pfeiffer

Despite their advantages in terms of computational resources, latency, and power consumption, event-based implementations of neural networks have not been able to achieve the same performance figures as their equivalent state-of-the-art deep network models.

Prediction of Manipulation Actions

no code implementations3 Oct 2016 Cornelia Fermüller, Fang Wang, Yezhou Yang, Konstantinos Zampogiannis, Yi Zhang, Francisco Barranco, Michael Pfeiffer

In psychophysical experiments, we evaluated human observers' skills in predicting actions from video sequences of different length, depicting the hand movement in the preparation and execution of actions before and after contact with the object.

Training Deep Spiking Neural Networks using Backpropagation

no code implementations31 Aug 2016 Jun Haeng Lee, Tobi Delbruck, Michael Pfeiffer

Deep spiking neural networks (SNNs) hold great potential for improving the latency and energy efficiency of deep neural networks through event-based computation.

Event-based vision

Precise neural network computation with imprecise analog devices

no code implementations23 Jun 2016 Jonathan Binas, Daniel Neil, Giacomo Indiveri, Shih-Chii Liu, Michael Pfeiffer

The operations used for neural network computation map favorably onto simple analog circuits, which outshine their digital counterparts in terms of compactness and efficiency.

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

no code implementations21 Nov 2015 Philipp Kainz, Michael Pfeiffer, Martin Urschler

Segmentation of histopathology sections is an ubiquitous requirement in digital pathology and due to the large variability of biological tissue, machine learning techniques have shown superior performance over standard image processing methods.

General Classification Semantic Segmentation

Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers

no code implementations2 Nov 2015 Jonathan Binas, Giacomo Indiveri, Michael Pfeiffer

Solving constraint satisfaction problems (CSPs) is a notoriously expensive computational task.

STDP enables spiking neurons to detect hidden causes of their inputs

no code implementations NeurIPS 2009 Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass

We show here that STDP, in conjunction with a stochastic soft winner-take-all (WTA) circuit, induces spiking neurons to generate through their synaptic weights implicit internal models for subclasses (or causes") of the high-dimensional spike patterns of hundreds of pre-synaptic neurons.

Dimensionality Reduction

Hebbian Learning of Bayes Optimal Decisions

no code implementations NeurIPS 2008 Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass

Uncertainty is omnipresent when we perceive or interact with our environment, and the Bayesian framework provides computational methods for dealing with it.

Bayesian Inference Decision Making +2

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