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BEGIN:VEVENT
UID:7200@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20181119T000000
DTEND;TZID=Europe/Paris:20181123T000000
DTSTAMP:20241211T192925Z
URL:https://www.i2m.univ-amu.fr/evenements/itwist-international-traveling-
 workshop-on-interactions-between-low-complexity-data-models-and-sensing-te
 chniques-4th-edition/
SUMMARY:Workshop (CIRM\, Luminy\, Marseille): iTWIST: international Traveli
 ng Workshop on Interactions between low-complexity data models and Sensing
  Techniques (4th edition)
DESCRIPTION:Workshop: WORKSHOP AND DOCTORAL SCHOOL\n4th edition.\n-\nThe ad
 vent of increased computing capabilities\, along with recent theoretical a
 nd numerical breakthroughs in the fields of signal processing\, computatio
 nal harmonic analysis\, inverse problem solving\, high-dimensional statist
 ics and convex optimization\, have boosted interactions between low-comple
 xity data models (e.g.\, sparse or low-rank data models) and novel data se
 nsing techniques.\nIn a nutshell\, low-complexity data models aim at captu
 ring\, modeling and exploiting “just the information you need” in the 
 ubiquitous data deluge characterizing any scientific or technological achi
 evements. High dimensional objects can be thus reconstructed using little 
 information. However\, further developments and novel ideas are still requ
 ired to meet new challenges\, especially for efficiently dealing with comp
 lex data structures of "real life" applications and for interconnecting su
 ch models with other theoretical and applied fields.\n​The iTWIST worksh
 op aims at fostering collaboration between international scientific teams 
 for developing new theories\, applications and generalizations of low-comp
 lexity models. This is why this event emphasizes dissemination of ideas th
 rough both specific oral/poster presentations and free discussions.\nFor t
 his edition\, ITWIST will be divided into two parts\, a 2-day doctoral sch
 ool\, followed by the actual workshop for three days.\n-\nSubjects :\n- Ap
 plication of low-complexity models in non-convex/non-linear inverse proble
 ms such as phase retrieval\, blind deconvolution\, blind calibration\, or 
 dictionary learning.\n- Machine learning\, deep learning\, compressive sta
 tistical learning and generalized high-dimensional inference of low-comple
 xity statistical models.\n- Optimization methods for recovering low-comple
 xity data representations (e.g.\, low-rank or tensor decomposition).\n- In
 formation theory\, high-dimensional (convex) geometry and randomness.\n- N
 ovel definition of low-complexity models (discrete-valued signals\, co-spa
 rsity\, mixed/group norm\, model-based\, tensor/manifold models).\n- Sensi
 ng and processing of low-complexity signals on manifolds and graphs.\n- Co
 mplexity and accuracy tradeoffs in numerical methods/optimization.\n- Simu
 lation and/or developments of novel electronic/optical compressive sensors
 .\n-\nOrganizing Committee:\nSandrine Anthoine (Aix-Marseille Université\
 , France)\, General Chair\nYannick Boursier (CPPM\, France)\nLaurent Jacqu
 es (UCLouvain\, Belgium)\nChristine De Mol (Université Libre de Bruxelles
 \, Belgium)\n-\nPartners :\n- Aix-Marseille Université (FIR colloques AMU
 )\n- Centre International de Rencontres Mathématiques (CIRM)\n- Centre Na
 tional de la Recherche Scientifique (CNRS)\n- ED184\n- FNRS\n- GDR MIA\n- 
 GDR MOA\n- IN2P3\n- Institut de Mathématiques de Marseille (I2M)\n- LabEx
  Archimède\n- LabEx CARMIN\n\n-\n\nSite web du workshop\n-
ATTACH;FMTTYPE=image/jpeg:https://www.i2m.univ-amu.fr/wp-content/uploads/2
 018/11/image_alea-si-hal-01278102-biphotonic_microscop_views-fig.4-anthoin
 e_and_al-x400.jpg
CATEGORIES:École ou Master class,Workshop
LOCATION:Luminy - CIRM\, 163 Avenue de Luminy\, Marseille\, 13009\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=163 Avenue de Luminy\, Mars
 eille\, 13009\, France;X-APPLE-RADIUS=100;X-TITLE=Luminy - CIRM:geo:0,0
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TZID:Europe/Paris
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DTSTART:20181028T020000
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