Christoffer Löffler

Christoffer Löffler
Friedrich-Alexander-University of Erlangen-Nürnberg | FAU · Artificial Intelligence in Biomedical Engineering

Master of Science

About

19
Publications
7,643
Reads
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63
Citations
Citations since 2016
15 Research Items
60 Citations
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Introduction
I am a PhD student at Björn Eskofier's MaD Lab and Fraunhofer IIS where I work on machine learning for time series data.
Additional affiliations
December 2021 - March 2022
Georgia Institute of Technology
Position
  • Visiting Scholar
Description
  • Visiting scholar at Prof. Rozell's Sensory Information Processing Lab (SIPLab) at the Machine Learning Center at Georgia Tech (ML@GT).
February 2019 - present
Friedrich-Alexander-University of Erlangen-Nürnberg
Position
  • Research Associate
Description
  • I'm working on my PhD at the MaD Lab.
January 2015 - present
Fraunhofer Institute for Integrated Circuits IIS
Position
  • Researcher
Description
  • At Fraunhofer I've worked on optical positioning, complex event processing, and large scale virtual reality. Currently I investigate machine learning for time-series data.
Education
January 2013 - December 2014
October 2009 - December 2012

Publications

Publications (19)
Preprint
Full-text available
Humans innately measure distance between instances in an unlabeled dataset using an unknown similarity function. Distance metrics can only serve as proxy for similarity in information retrieval of similar instances. Learning a good similarity function from human annotations improves the quality of retrievals. This work uses deep metric learning to...
Preprint
Full-text available
The correct interpretation and understanding of deep learning models is essential in many applications. Explanatory visual interpretation approaches for image and natural language processing allow domain experts to validate and understand almost any deep learning model. However, they fall short when generalizing to arbitrary time series data that i...
Article
This work proposes metric learning for fast similarity-based scene retrieval of unstructured ensembles of trajectory data from large databases. We present a novel representation learning approach using Siamese Metric Learning that approximates a distance preserving low-dimensional representation and that learns to estimate reasonable solutions to t...
Article
Full-text available
Active learning prioritizes the labeling of the most informative data samples. However, the performance of active learning heuristics depends on both the structure of the underlying model architecture and the data. We propose IALE, an imitation learning scheme that imitates the selection of the best-performing expert heuristic at each stage of the...
Chapter
Full-text available
Despite the ongoing automation of modern production processes manual labor continues to be necessary due to its flexibility and ease of deployment. Automated processes assure quality and traceability, yet manual labor introduces gaps into the quality assurance process. This is not only undesirable but even intolerable in many cases.
Conference Paper
Full-text available
Given the presence of deep neural networks (DNNs) in all kinds of applications, the question of optimized deployment is becoming increasingly important. One important step is the automated size reduction of the model footprint. Of all the methods emerging, post-training quantization is one of the simplest to apply. Without needing long processing o...
Conference Paper
Full-text available
The ongoing automation of modern production processes requires novel human-computer interaction concepts that support employees in dealing with the unstoppable increase in time pressure, cognitive load, and the required fine-grained and process-specific knowledge. Augmented Reality (AR) systems support employees by guiding and teaching work process...
Preprint
Active learning (AL) prioritizes the labeling of the most informative data samples. As the performance of well-known AL heuristics highly depends on the underlying model and data, recent heuristic-independent approaches that are based on reinforcement learning directly learn a policy that makes use of the labeling history to select the next sample....
Conference Paper
Full-text available
Visual Odometry (VO) accumulates a positional drift in long-term robot navigation tasks. Although Convolutional Neural Networks (CNNs) improve VO in various aspects, VO still suffers from moving obstacles, discontinuous observation of features, and poor textures or visual information. While recent approaches estimate a 6DoF pose either directly fro...
Preprint
Full-text available
Visual Odometry (VO) accumulates a positional drift in long-term robot navigation tasks. Although Convolutional Neural Networks (CNNs) improve VO in various aspects, VO still suffers from moving obstacles, discontinuous observation of features, and poor textures or visual information. While recent approaches estimate a 6DoF pose either directly fro...
Conference Paper
Full-text available
Outside-in camera-based localization systems determine the position of mobile objects (with markers attached to them) by observing a tracking area with multiple camera anchors. Up to now the identification of the objects in the camera images only works for close objects (with passive 3- dimensional marker constellations) or when tracking gaps or a...
Conference Paper
Full-text available
Real-time tracking allows to trace goods and enables the optimization of logistics processes in many application areas. Camera-based inside-out tracking that uses an infrastructure of fixed and known markers is costly as the markers need to be installed and maintained in the environment. Instead, systems that use natural markers suffer from changes...
Patent
Full-text available
A method (100) to determine a present position (122) of an object (600). The method (100) comprises using (102) an optical positioning system (104) to determine a first preliminary position (112) and using (106) a radio-based positioning system (108) to determine a second preliminary position (114), determining (110) a supposed position (116) on th...
Conference Paper
Full-text available
Event-Based Systems (EBS) can efficiently analyze large streams of sensor data in near-realtime. But they struggle with noise or incompleteness that is seen in the unprecedented amount of data generated by the Internet of Things. We present a generic approach that deals with uncertain data in the middleware layer of distributed event-based systems...
Conference Paper
Full-text available
The DEBS 2014 Grand Challenge targets the monitoring and prediction of energy loads of smart plugs installed in private households. This paper presents details of our middleware solution and efficient median calculation, shows how we address data quality issues, and provides insights into our enhanced prediction based on hidden Markov models. The e...
Chapter
Full-text available
With the global growth of the market for smartphones new business ideas and applications are developed continuously. These often utilize the resources of a mobile device to a considerable extent and reach the limits of these. In this work we focus on the simulation of an on-demand music service on a modern smartphone. Our simulation model includes...
Conference Paper
Full-text available
Event-based systems (EBS) are widely used to efficiently process massively parallel data streams. In distributed event processing the allocation of event detectors to machines is crucial for both the latency and efficiency, and a naive allocation may even cause a system failure. But since data streams, network traffic, and event loads cannot be pre...

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Projects

Projects (2)
Project
Zur Vernetzung von Forschung und Wirtschaft hat das Fraunhofer-Institut für Integrierte Schaltungen IIS in Kooperation mit der Friedrich-Alexander-Universität Erlangen-Nürnberg und der Ludwig-Maximilians-Universität München unter weiterer Beteiligung der Fraunhofer-Institute IKS und IISB eine einzigartige Forschungsinfrastruktur in Bayern geschaffen: das ADA Lovelace Center for Analytics, Data and Applications. Es verbindet als Kooperationsplattform für Wissenschaft und Wirtschaft auf innovative Art KI-Forschung mit KI-Anwendungen. Das Besondere am ADA Lovelace Center ist tatsächlich die enge Verbindung aus Forschung und industrieller Anwendung: Mit der Art und Weise, wie wir unsere Kompetenzen, Methoden und Verfahren aus dem Bereich der Künstlichen Intelligenz in den Fragestellungen der Praxis einsetzen und an ihnen weiterentwickeln, wie wir an Projekte herangehen und wie wir mit unseren Partnern aus Industrie und Wissenschaft zusammenarbeiten, wollen wir den Zugang der Unternehmen zu umfassender KI-Expertise erleichtern und so schnell konkreten Nutzen für sie herstellen; und zwar über die Grenze des derzeit Machbaren hinaus. So wollen wir Unternehmen vom enormen Potenzial von KI überzeugen und KI in die industrielle Anwendung bringen: Das ADA Lovelace Center versteht sich als Multiplikator, um KI-Kompetenz in einem Unternehmen aufzubauen oder die vorhandene KI-Kompetenz zu stärken bzw. weiterzuentwickeln. Das wissenschaftliche Methodenspektrum, das wir im ADA Lovelace Center dafür einsetzen, ist sehr breit und die Auswahl der richtigen Methode abhängig vom Anwendungsfall. Immer aber geht es bei uns um Datenanalyse: von der klassischen Zustandsbeschreibung über Vorhersagen von Ereignissen bis hin zu entscheidungsbasierten Methoden, die z.B. automatisiert eine bestimmte Handlung auslösen sollen. Dazu binden wir übrigens neben den genannten regionalen Playern auch viele andere nationale und internationale Wissenschaftspartner ein. An folgenden Kompetenzsäulen wird geforscht: • Automatisches Lernen • Sequenzbasiertes Lernen • Erfahrungsbasiertes Lernen • Few Labels Learning • Erklärbares Lernen • Mathematische Optimierung • Semantik • Few Data Learning Eine Beschreibung zu den oben genannten Kompetenzsäulen kann unter diesem Link https://www.scs.fraunhofer.de/de/referenzen/ada-center.html#381039553 gefunden werden.
Project
The goal of the project is to develop methods that enable robust localization. With the help of machine learning, positions are estimated automatically by using reference measurements as experience data. The measurements of different (inertial-, image-, and radio-based) localization systems are used as raw data, on the basis of which deep neural networks are trained, which replace parts or entire classic positioning pipelines.