Poster – Automated Soccer Scene Tracking Using Deep Neural Networks Bodenstein, C., Goetz, M., Riedel, M., Automated Soccer Scene Tracking Using Deep Neural Networks, the Institute for Advanced Simulation (IAS) – Symposium, December 5 – 6, 2016, Juelich, Germany [ EVENT ] [ JUSER ] [ PDF (~ 3,48 MB) ]
TUTORIAL: Einführung in Maschinelles Lernen zur Datenanalyse Invited Tutorial (German language) Smart Data Innovation Conference, Karlsruhe Institute of Technology (KIT), Germany 2016-10-13 [ Event ] Abstract: Der Kurs vermittelt Grundlagen zur Analyse von Daten und ist an Kursbesucher gerichtet die keine Vorkenntnisse in diesem Bereich haben. Die Inhalte werden prinzipielle Techniken umfassen, um Methoden der Datenanalyse wie Clustering, Klassifikation oder Regression besser einzuordnen. Das beinhaltet auch ein Verständnis von Testdaten, Trainingsdaten und Validierungsdaten. Anhand von einfachen Beispielen werden weiterhin Probleme wie bspw. overfitting angesprochen sowie dessen Lösungsansätze Validierung und Regularisierung. Nach dem Kurs haben Teilnehmer das Verständnis wie man an...
Dr. Gabriele Cavallaro [Completed] Spectral-Spatial Classification of Remote Sensing Optical Data with Morphological Attribute Pro les using Parallel and Scalable Methods Gabriele Cavallaro, Doctoral Thesis University of Iceland, School of Engineering and Natural Sciences (SENS), Iceland, 2016 [ DEFENCE PHOTOS ] [ PERSONAL WEB PAGE ]
Scientific Big Data Analytics by HPC Thomas Lippert, Daniel Mallmann, Morris Riedel Publication Series of the John von Neumann Institute for Computing (NIC) NIC Series 48, 417, ISBN 978-3-95806-109-5, pp. 1 – 10, 2016 [ PID ] [ Juelich ] Abstract: Storing, managing, sharing, curating and especially analyzing huge amounts of data face an immense visibility and importance in industry and economy as well as in science and research. Industry and economy exploit ’Big Data’ for predictive analysis, to increase the efficiency of infrastructures, customer segmentation, and tailored services. In science, Big Data allows for addressing problems with complexities that...
HPDBSCAN – Highly Parallel DBSCAN Goetz, M., Bodenstein, C., Riedel, M.: HPDBSCAN – Highly Parallel DBSCAN, in conference proceedings of ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2015), Machine Learning in HPC Environments (MLHPC 2015) Workshop, November 15-20, 2015, Austin, Texas, USA [ EVENT ] [ DOI ] [ JUSER ] [ RESEARCHGATE ] Abstract: Clustering algorithms in the field of data-mining are used to aggregate similar objects into common groups. One of the best-known of these algorithms is called DBSCAN. Its distinct design enables the search for an apriori unknown number of arbitrarily shaped...
On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods Gabriele Cavallaro, Morris Riedel, Matthias Richerzhagen, Jon Atli Benediktsson, Antonio Plaza IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Issue 99, pp. 1-13, 2015 [ DOI ] [ Juelich ] Abstract: Owing to the recent development of sensor resolutions onboard different Earth observation platforms, remote sensing is an important source of information for mapping and monitoring natural and man-made land covers. Of particular importance is the increasing amounts of available hyperspectral data originating from airborne and satellite sensors such as AVIRIS,...
University of Iceland University Ball 2019 – Árshátíð Háskóla Islands 2019 Selected Impressions from last weeks nice University of Iceland annual Ball @Haskoli_Islands @uni_iceland @Iceland @uisens celebrating our long German-Iceland collaboration with @fz_juelich @fzj_jsc @helmholtz_ai @DEEPprojects @EOSC_Nordic #ON4OFF #SMITH.https://t.co/RTcv9PUBdS pic.twitter.com/79Kjv5ljjC — Morris Riedel (@MorrisRiedel) November 17, 2019 Sieh dir diesen Beitrag auf Instagram an Selected Impressions from last weeks nice University of Iceland annual Ball @haskoli_islands @the_effective_communicators @Iceland @von_hi celebrating our long German-Iceland collaboration with @forschungszentrum_juelich #julichsupercomputingcenter @helmholtz_de #AI #DEEPprojects #EOSC_Nordic #ON4OFF #SMITH . http://www.morrisriedel.de/university-of-iceland Ein Beitrag geteilt von Morris Riedel (@morrisriedel) am Nov 17, 2019 um 4:59 PST University...
Improvements of the UltraScan Scientific Gateway to Enable Computational Jobs on Large-scale and Open-standards based Cyberinfrastructures Memon, M.S., Attig, N., Demeler, B., Gorbet, G., Girmshaw, A., Gunathilake, L., Janetzko, F., Lippert, T., Marru, S., Singh, R., Riedel, M.: Improvements of the UltraScan Scientific Gateway to enable Computational Jobs on Large-scale and Open-standards based Cyberinfrastructures, in conference proceedings of the 2013 XSEDE Conference on Extreme Science and Engineering Discovery Environment Gateway to Discovery (XSEDE 2013), July 22-25, 2013, San Diego, California, USA [ EVENT ] [ DOI ] [ JUSER ] [ RESEARCHGATE ] Abstract: The UltraScan data analysis application is...
Lectures by Students Lecture at an International Conference Goetz, M., Practice & Experience with Scalable Clustering Algorithms for Statistical Earth Science Data Mining, European Geosciences Union General Assembly (EGU 2015), April 12 – 17, 2015, Vienna, Austria [ JUSER ] Goetz, M., HPDBSCAN – Highly parallel DBSCAN, Workshop on Machine Learning in High-Performance Computing Environments (MLHPC 2015) at International Supercomputing Conference (SC 2015), November 15 – 20, 2015, Austin, Texas, USA [ EVENT ] Lecture at an Academic Symposium, Seminar or Forum for Academic Groups Goetz, M., Von parallelem Clustering und besoffenen Fliegen, Jahresabschlusskolloquium des JSC, December 17, 2015, Juelich,...
Digital Library Deep Learning and Physical Models Wiewel, S., Becher, M., Thuerey, N.: Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow, arXiv, submitted to NIPS 2018, 2018 Abstract: Our work explores methods for the data-driven inference of temporal evolutions of physical functions with deep learning techniques. More specifically, we target fluid flow problems, and we propose a novel LSTM-based approach to predict the changes of the pressure field over time. The central challenge in this context is the high dimensionality of Eulerian space-time data sets. Key for arriving at a feasible algorithm is a technique for dimensionality reduction...
https://orcid.org/0000-0003-1810-9330