Cecile Germain
Présentation
Publications
Publications
|
|
Deep learning for certification of the quality of the data acquired by the CMS Experiment19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Mar 2019, Saas-Fee, Switzerland. pp.012045, ⟨10.1088/1742-6596/1525/1/012045⟩ |
|
|
Systematic aware learning - A case study in High Energy Physics23rd International Conference on Computing in High Energy and Nuclear Physics, Jul 2018, Sofia, Bulgaria. pp.06024, ⟨10.1051/epjconf/201921406024⟩ |
|
|
TrackML : a tracking Machine Learning challenge39th International Conference on High Energy Physics, Jul 2018, Seoul, South Korea. pp.159, ⟨10.22323/1.340.0159⟩ |
|
|
Anomaly Detection With Conditional Variational AutoencodersICMLA 2019 - 18th IEEE International Conference on Machine Learning and Applications, Dec 2019, Boca Raton, United States. pp.1651-1657, ⟨10.1109/ICMLA.2019.00270⟩ |
|
|
Anomaly Detection With Conditional Variational AutoencodersEighteenth International Conference on Machine Learning and Applications, Dec 2019, Boca Raton, United States |
|
|
Trigger Rate Anomaly Detection with Conditional Variational Autoencoders at the CMS ExperimentMachine Learning and the Physical Sciences Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS), Dec 2019, Vancouver, Canada |
|
|
The TrackML high-energy physics tracking challenge on Kaggle23rd International Conference on Computing in High Energy and Nuclear Physics, Jul 2018, Sofia, Bulgaria. pp.06037, ⟨10.1051/epjconf/201921406037⟩ |
|
|
Systematics aware learning: a case study in High Energy PhysicsESANN 2018 - 26th European Symposium on Artificial Neural Networks, Apr 2018, Bruges, Belgium |
TrackML: A High Energy Physics Particle Tracking Challenge14th International Conference on e-Science, Oct 2018, Amsterdam, Netherlands. pp.344, ⟨10.1109/eScience.2018.00088⟩ |
|
|
|
The TrackML challengeNIPS 2018 - 32nd Annual Conference on Neural Information Processing Systems, Dec 2018, Montreal, Canada. pp.1-23 |
|
|
Adversarial learning to eliminate systematic errors: a case study in High Energy PhysicsNIPS 2017 - workshop Deep Learning for Physical Sciences, Dec 2017, Long Beach, United States. pp.1-5 |
|
|
Track reconstruction at LHC as a collaborative data challenge use case with RAMPConnecting The Dots / Intelligent Tracker, Mar 2017, Orsay, France. pp.00015, ⟨10.1051/epjconf/201715000015⟩ |
|
|
How Machine Learning won the Higgs Boson ChallengeEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2016, Bruges, Belgium |
TrackML: a LHC Tracking Machine Learning ChallengeInternational Conference on Computing in High Energy and Nuclear Physics, Oct 2016, San Francisco, United States |
|
|
|
The Higgs Machine Learning Challenge21st International Conference on Computing in High Energy and Nuclear Physics (CHEP 2015), Apr 2015, Okinawa, Japan. pp.072015, ⟨10.1088/1742-6596/664/7/072015⟩ |
|
|
Certifying the interoperability of RDF database systemsLDQ 2015 - 2nd Workshop on Linked Data Quality, May 2015, Portorož, Slovenia |
|
|
The Higgs boson machine learning challengeNIPS 2014 Workshop on High-energy Physics and Machine Learning, Dec 2014, Montreal, Canada. pp.37 |
|
|
A platform for scientific data sharingBDA2015 - Bases de Données Avancées, Sep 2015, Île de Porquerolles, France |
|
|
Sequential fault monitoringCloud and Autonomic Computing, Sep 2014, London, United Kingdom |
|
|
TFT, Tests For TriplestoresSemantic Web Challenge, part of the International Semantic Web Conference, Oct 2014, Riva Del Garda, Italy |
The ATLAS Higgs Boson Machine Learning ChallengeInternational Conference on High Energy Physics(ICHEP) Conference, Jul 2014, Valencia, Spain |
|
Introduction to the HEPML Workshop and the HiggsML challengeHEPML workshop at NIPS14 - Neural Information Processing Systems Conference, Dec 2014, Montreal, Canada |
|
|
|
Distributed Monitoring with Collaborative Prediction12th IEEE International Symposium on Cluster, Cloud and Grid Computing (CCGrid'12), May 2012, Ottawa, Canada. epub ahead of print |
|
|
Efficient fault monitoring with Collaborative Predictionjournées scientifiques mésocentres et France Grilles 2012, Oct 2012, Paris, France |
|
|
The Grid ObservatoryIEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, May 2011, Newport Beach, United States |
|
|
Non-Markovian Reinforcement Learning for Reactive Grid schedulingConférence Francophone d'Apprentissage, May 2011, Chambéry, France |
|
|
The Green Computing Observatory: a data curation approach for green ITInternational Conference on Cloud and Green Computing, Dec 2011, Sydney, Australia |
|
|
Characterizing E-Science File Access Behavior via Latent Dirichlet Allocation4th IEEE International Conference on Utility and Cloud Computing (UCC 2011), Dec 2011, Melbourne, Australia |
|
|
Modèles comportementaux de la grille : enjeux et exemplesPremières journées scientifiques France-Grilles, Sep 2011, Lyon, France |
|
|
Modèles comportementaux de la grille : enjeux et exemplesRencontres Scientifiques France Grilles 2011, Sep 2011, Lyon, France |
|
|
Discovering Piecewise Linear Models of Grid Workload10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, May 2010, Melbourne, Australia. pp.474-484 |
Performance evaluation of the Estimated Response Time strategy: tools, methods and an experimentEGEE User Forum, Apr 2010, Uppsala, Sweden |
|
|
|
Adaptively Detecting Changes in Autonomic Grid ComputingProcs of ACS 2010, Oct 2010, Belgium. http://wiki.esi.ac.uk/ACS2010 |
Visualizing the dynamics of e-science social networksEGEE User Forum, Apr 2010, Uppsala, Sweden |
|
|
|
Toward Autonomic Grids: Analyzing the Job Flow with Affinity Streaming15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Jun 2009, Paris, France |
One year of the Grid ObservatoryEGEE'09, Sep 2009, Barcelone, Spain |
|
|
|
Responsive Elastic Computing2009 ACM/IEEE Conference on International Conference on Autonomic Computing, Jun 2009, Barcelone, Spain. pp.55-64, ⟨10.1145/1555301.1555311⟩ |
|
|
Multi-scale Real-time Grid Monitoring with Job Stream MiningCCGrid, May 2009, Shanghai, China |
|
|
Grid Differentiated Services: a Reinforcement Learning Approach8th IEEE International Symposium on Cluster Computing and the Grid, May 2008, Lyon, France |
|
|
Utility-based Reinforcement Learning for Reactive GridsThe 5th IEEE International Conference on Autonomic Computing, May 2008, Chicago, United States |
|
|
Le modelage des travaux d'un Système de Grille16th congrès francophone AFRIF-AFIA Reconnaissance des Formes et Intelligence Artificielle (RFIA), Jan 2008, Amiens, France |
Towards a statistical model of EGEE load3rd EGEE User Forum, EGEE-II project, Feb 2008, Clermont-Ferrand, France |
|
|
|
Predicting Bounds on Queuing Delay in the EGEE grid2nd EGEE User Forum, May 2007, Manchester, United Kingdom |
|
|
Toward Behavioral Modeling of a Grid System: Mining the Logging and Bookkeeping filesDSMM07, Oct 2007, Omaha, United States |
Fault Detection and Diagnosis from the Logging and Bookkeping Data2nd EGEE User Forum, May 2007, Manchester, United Kingdom |
|
|
|
Grid Scheduling for Interactive AnalysisHealthGrid 2006, Jun 2006, Valencia, Spain. pp.25-33 |
Scheduling for Interactive GridsFirst EGEE User Forum, Mar 2006, Genève, Switzerland |
|
|
|
Grid-enabling medical image analysisCluster Computing and Grid 2005 (CCGrid05) Bio-Medical Computations on the Grid (Bio-Grid), May 2005, Cardiff, United Kingdom. pp.339-349, ⟨10.1007/s10877-005-0679-9⟩ |
The Tracking Machine Learning ChallengeNIPS workshop: Challenges in Machine Learning (CiML), Dec 2016, Barcelona, Spain |
|
|
|
Data acquisition for analytical platforms: Automating scientific workflows and building an open database platform for chemical anlysis metadataChimiométrie XVII, Jan 2016, Namur, Belgium |
|
|
Summary of the Weizmann workshop: Hammers & Nails - Machine Learning & HEP2017 - Hammers & Nails - Machine Learning & HEP, Jul 2017, Rehovot, Israel. , pp.1-48, 2017 |
|
|
Robust deep learning: A case studyJDSE 2017 - 2nd Junior Conference on Data Science and Engineering, Sep 2017, Orsay, France. , pp.1-5, 2017 |
Les occupations néolithiques de "la Bruyère du Hamel" à Condé-sur-Ifs (Calvados)Société préhistorique française, 62, 2016, Mémoires de la Société Préhistorique Française, 978-2-913745-68-1 |
|
|
Data Quality Monitoring Anomaly DetectionArtificial Intelligence for High Energy Physics, World Scientific, pp.115-149, 2022, ⟨10.1142/9789811234033_0005⟩ |
|
|
Grid Analysis of Radiological DataMario Cannataro (Ed.). Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine and Healthcare, IGI, pp.363-391, 2009, chapter 19, ⟨10.4018/978-1-60566-374-6.ch019⟩ |
|
|
The TrackML Particle Tracking Challenge2018 |
|
|
The Grid Observatory 3.0 - Towards reproducible research and open collaborations using semantic technologies2015 |
Learning to discover: the Higgs boson machine learning challengeAutre publication scientifique hal-01104487v1 |
|
|
|
Efficient fault monitoring with Collaborative Prediction2012 |
|
|
The Tracking Machine Learning challenge : Accuracy phase2019 |
|
|
Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider2019 |
|
|
Discovering Linear Models of Grid Workload[Research Report] RR-7112, INRIA. 2009 |