Christoffer Löffler

I am a PhD student at Björn Eskofier's MaD Lab and Fraunhofer IIS where I work on machine learning for time series data in the ADA Lovelace Center.

At Fraunhofer I've worked on optical positioning, complex event processing, and large scale virtual reality. I did my Bachelors and Masters at Friedrich-Alexander University Erlangen-Nürnberg (FAU), where I was advised by Christopher Mutschler, back then researching at Michael Philippsen's Programming Systems lab. These collaborations led to a Dijkstra number of four and an Erdös number of five!

Email  /  @MaD Lab  /  Google Scholar (81)  /  ResearchGate  /  ORCID  /  Facebook  /  Github  /  Twitter  /  LinkedIn  /  Youtube

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Research

I'm interested in machine learning with few labeled data (like active learning) and building interactive systems for the real-world.

IALE: Imitating Active Learner Ensembles
Christoffer Löffler, Christopher Mutschler
Journal Track @ Neural Information Processing Systems (NeurIPS), New Orleans, LA, 11/2022
code / pdf / poster / video

We propose an imitation learning approach that learns a policy for active learning from an ensemble of deep active learners.

Active Learning of Ordinal Embeddings: A User Study on Football Data
Christoffer Löffler, Kion Fallah, Stefano Fenu, Dario Zanca, Bjoern Eskofier, Christopher J. Rozell, Christopher Mutschler
under review, preprint available , 07/2022
preprint

This work uses deep metric learning and active learning to learn humans' innate similarity functions from few annotations.

Don't Get Me Wrong: How to apply Deep Visual Interpretations to Time Series
Christoffer Löffler, Wei-Cheng Lai, Björn M. Eskofier, Dario Zanca, Lukas Schmidt, Christopher Mutschler
under review, preprint available, 03/2022
code / preprint

A framework of six orthogonal metrics for gradient- or perturbation-based post-hoc visual interpretation methods. Its designed for time series classification and segmentation tasks.

IALE: Imitating Active Learner Ensembles
Christoffer Löffler, Christopher Mutschler
Journal of Machine Learning Research 23, 02/2022
code / pdf

We propose an imitation learning approach that learns a policy for active learning from an ensemble of deep active learners.

Deep Siamese Metric Learning: A Highly Scalable Approach to Searching Unordered Sets of Trajectories
Christoffer Löffler, Luca Reeb, Daniel Dzibela, Robert Marzilger, Nicolas Witt, Björn M. Eskofier, Christopher Mutschler
ACM Transactions on Intelligent Systems and Technology, Special Issue on Intelligent Trajectory Data Analytics, 02/2022.
3 citations / pdf / project / DOI: 10.1145/3465057

We enable interactive search in unordered sets of trajectories, with a focus on team sports.

Recipes for Post-training Quantization of Deep Neural Networks
Ashutosh Mishra, Christoffer Löffler, Axel Plinge
EMC^2: Workshop on Energy Efficient Machine Learning and Cognitive Computing, 2020
Axel's video / Ashutosh's video / pdf / project

We show that post-training quantization (done greedily) benefits from an optimal global bit-width and evaluate this on VGG, ResNet, UNet and our tool tracking FCN.

A Sense of Quality for Augmented Reality Assisted Process Guidance
Anes Redzepagic, Christoffer Löffler, Tobias Feigl, Christopher Mutschler
IEEE Intl. Symposium on Mixed and Augmented Reality (ISMAR), poster track, 2020
3 citations / video presentation / project / DOI: 10.1109/ISMAR-Adjunct51615.2020.00046

We combine inertial sensors, mounted on work tools, with AR-headsets to enrich modern assistance systems with a sense of process quality, powered by machine learning.

Automated Quality Assurance for Hand-held Tools via Embedded Classification and AutoML
Christoffer Löffler, Christian Nickel, Christopher Sobel, Daniel Dzibela, Jonathan Braat, Benjamin Gruhler, Philipp Woller, Nicolas Witt, Christopher Mutschler
European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), demo track, 2020
5 citations / demo video / video presentation / project / pdf / DOI: 10.1007/978-3-030-67670-4_33

We describe an AutoML system for our custom hardware and classify multivariate data using deep and shallow methods.

ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization
Felix Ott, Tobias Feigl, Christoffer Löffler, Christopher Mutschler,
Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops, workshop track, 2020
10 citations / video / pdf / DOI: 10.1109/CVPRW50498.2020.00029

Learning to fuse absolute poses (6 degrees of freedom) with optical flow (e.g., FlowNet) to improve a mobile agent's self positioning.

Localization Limitations of ARCore, ARKit, and Hololens in Dynamic Large-scale Industry Environments.
Tobias Feigl, Andreas Porada, Steve Steiner, Christoffer Löffler, Christopher Mutschler, Michael Philippsen
15th Intl. Conf. on Computer Graphics Theory and Applications (GRAPP), 2020
25 citations / pdf / DOI: 10.5220/0008989903070318

An evaluation of the big AR systems for real use-cases.

Evaluation criteria for inside-out indoor positioning systems based on machine learning
Christoffer Löffler, Sascha Riechel, Janina Fischer, Christopher Mutschler
IEEE Intl. Conf. on Indoor Positioning and Indoor Navigation (IPIN), 2018
13 citations / project / warehouse dataset / pdf / DOI: 10.1109/IPIN.2018.8533862

Using reference positioning systems, we record a multi-camera dataset with exact labels and propose criteria for evaluating indoor positioning.

Optical Camera Communication for Active Marker Identification in Camera-based Positioning Systems
Lorenz Gorse, Christoffer Löffler, Christopher Mutschler, Michael Philippsen
IEEE 15th Workshop on Positioning, Navigation and Communications (WPNC), 2018
pdf / DOI: 10.1109/WPNC.2018.8555846

How to build a cheap but reliable optical positioning system with Raspberry Pi with active LED markers and continuously identify them.

Approximative event processing on sensor data streams
Christoffer Löffler, Christopher Mutschler, Michael Philippsen
Proc. of the 9th ACM Intl. Conf. on Distributed Event-Based Systems (DEBS), poster track, best demo/poster award, 2015
1 citation / pdf / DOI: 10.1145/2675743.2776767

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

Predictive load management in smart grid environments
Christopher Mutschler, Christoffer Löffler, Nicolas Witt, Thorsten Edelhäußer, Michael Philippsen
Proc. of the 8th ACM Intl. Conf. on Distributed Event-Based Systems (DEBS), 2014
5 citations / pdf / DOI: 10.1145/2611286.2611330

We've won the DEBS 2014 Grand Challenge with our hidden Markov model approach.

Simulating the energy management on smartphones using hybrid modeling techniques
Ibrahim Alagöz, Christoffer Löffler, Vitali Schneider, Reinhard German
GI/ITG Intl. Conf. on Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance (MMB & DFT), best student paper award, 2014
5 citations / pdf / DOI: 10.1007/978-3-319-05359-2_15

We model a smartphone playing back music and its energy management.

Evolutionary algorithms that use runtime migration of detector processes to reduce latency in event-based systems
IEEE NASA/ESA Conf. on Adaptive Hardware and Systems (AHS), 2013
Christoffer Löffler, Christopher Mutschler, Michael Philippsen
4 citations / pdf / DOI: 10.1109/AHS.2013.6604223

When running a distributed low-latency event processing system, you may want to optimize latency - heuristically.

Current Google Scholar citation statistics.
Patents
Method to determine a present position of an object, positioning system, tracker and computer program
Stephan Otto, Tobias Feigl, Christian Daxer, Alexander Bruckmann, Christoffer Löffler, Christopher Mutschler, Marc Faßbinder
Filed 2017-12-11. Published 2020-11-26. Pending.
Google Patents

Combination of radar systems with cameras.

Apparatus, method and computer program for improving the performance of an event-based distributed analysis system
Christopher Mutschler, Christoffer Löffler
Filed 2012-12-13. Published 2019-04-25. Active.
Google Patents

Method for improving the performance of an event-based distributed analysis system.

Teaching
DRL seminar, deep reinforcement learning, summer 2019
Machine Learning for Timeseries (project), winter 2019-2021
Girls/Youth and Technology, summer 2019

SemML, seminar on machine learning, winter 2015-2021
Seminar Event Processing, summer 2016

This website is forked from here, thank you Jon Barron.