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When: Jun 2, 2022 - Jun 2, 2022 Submission Deadline: Apr 4, 2022 | ||
3rd ISC HPC International Workshop on Monitoring & Operational Data Analytics (MODA22) • June 02, 2022, Hamburg, Germany • Website: moda.dmi.unibas.ch • Submission: https://easychai... | ||
HIGH PERFORMANCE COMPUTING DATA ANALYTICS MACHINE LEARNING DATA CENTER | ||
![]() UCAI 2020 : Workshop on User-Centered Artificial IntelligenceMagdeburg, Germany |
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When: Sep 06, 2020 - Sep 06, 2020 Submission Deadline: Jun 05, 2020 | ||
The proliferation of AI-based techniques poses a range of new challenges for the design and engineering of interactive and adaptive systems since they tend to act as black boxes and do not offer the u... | ||
HUMAN-COMPUTER INTERACTION ARTIFICIAL INTELLIGENCE MACHINE LEARNING | ||
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When: Jun 25, 2020 - Jun 25, 2020 Submission Deadline: Mar 30, 2020 | ||
The combination of computational fluid dynamics (CFD) with machine learning (ML) is a newly emerging research direction with the potential to enable solving so far unsolved problems in many applicatio... | ||
MACHINE LEARNING HIGH PERFORMANCE COMPUTING SCIENTIFIC COMPUTING COMPUTATIONAL FLUID DYNAMIC | ||
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When: Jun 25, 2020 - Jun 25, 2020 Submission Deadline: Mar 01, 2020 | ||
The main scope of the 1st ISC-HPC International Workshop on Monitoring and Operational Data Analytics (MODA) is to provide insight on current trends in MODA, to identify potential gaps, and to offer a... | ||
HPC OPERATIONAL DATA ANALYTICS MONITORING MACHINE LEARNING | ||
![]() DLonSC 2020 : 4th International Workshop on Deep Learning on SupercomputersFrankfurt, Germany |
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When: Jun 25, 2020 - Jun 25, 2020 Submission Deadline: Mar 31, 2020 | ||
The Deep Learning on Supercomputers workshop is with ISC’20 on June 25th, 2020 in Frankfurt, Germany. It is the fourth workshop in the Deep Learning on Supercomputers series. The workshop provid... | ||
COMPUTER SCIENCE MACHINE LEARNING ARTIFICIAL INTELLIGENCE | ||
![]() AutoDS 2019 : ECMLPKDD WORKSHOP ON AUTOMATING DATA SCIENCEWurzburg, Germany |
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When: Sep 20, 2019 - Sep 20, 2019 Submission Deadline: Jun 14, 2019 | ||
ECMLPKDD WORKSHOP ON AUTOMATING DATA SCIENCE (ADS) • Wurzburg, Germany, Friday 20 September 2019 • https://sites.google.com/view/autods • ***** CALL FOR PAPERS ***** • Dates: ... | ||
DATA SCIENCE MACHINE LEARNING AUTOMATION ARTIFICIAL INTELLIGENCE | ||
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When: Sep 20, 2019 - Sep 20, 2019 Submission Deadline: Jun 14, 2019 | ||
Call for Papers and Datasets • Data and Machine Learning Approaches with Multiple Views • Workshop In conjunction with ECML-PKDD’19 • https://damvl.lis-lab.fr/ • Recent years... | ||
MACHINE LEARNING MULTIMODALITY DATASET MULTIVIEW LEARNING | ||
![]() MIDAS 2019 : 4th Workshop on MIning DAta for financial applicationSWurzburg, Germany |
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When: Sep 16, 2019 - Sep 16, 2019 Submission Deadline: Jun 7, 2019 | ||
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DATA MINING MACHINE LEARNING FINANCIAL APPLICATIONS | ||
![]() MLCS 2019 : Workshop on Machine Learning for CyberSecurityWurzburg, Germany |
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When: Sep 20, 2019 - Sep 20, 2019 Submission Deadline: Jun 21, 2019 | ||
Workshop on Machine Learning for CyberSecurity • (Co-located with ECMLPKDD 2019) • The last decade has been a critical one regarding cybersecurity, with studies estimating the cost of cybe... | ||
CYBERSECURITY MACHINE LEARNING | ||
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When: Sep 10, 2019 - Sep 10, 2019 Submission Deadline: Jun 9, 2019 | ||
The workshop will be held in Regensburg, Germany on September, 10th 2019 in conjunction with COSIT 2019 (14th International Conference on Spatial Information Theory). • ABOUT • Archaeologi... | ||
SPATIO-TEMPORAL DATA MODEL MACHINE LEARNING CULTURAL HERITAGE DIGITAL RECONSTRUCTION | ||
![]() MaxEnt 2019 : Workshop on Bayesian Inference and Maximum Entropy Methods in Science and EngineeringGarching/Munich, GERMANY |
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When: Jun 30, 2019 - Jul 5, 2019 Submission Deadline: Apr 30, 2019 | ||
Main topics of the workshop are the application of Bayesian inference and the maximum entropy principle to inverse problems in science, machine learning, information theory and engineering. • In... | ||
BAYESIAN INFERENCE MACHINE LEARNING INVERSE PROBLEMS BIG DATA | ||
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