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UID:5480@test.i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20181119T000000
DTEND;TZID=Europe/Paris:20181123T000000
DTSTAMP:20210708T143045Z
URL:https://test.i2m.univ-amu.fr/events/itwist-international-traveling-wor
kshop-on-interactions-between-low-complexity-data-models-and-sensing-techn
iques-4th-edition/
SUMMARY:iTWIST: international Traveling Workshop on Interactions between lo
w-complexity data models and Sensing Techniques (4th edition) - Workshop
DESCRIPTION:WORKSHOP AND DOCTORAL SCHOOL\n\n4th edition.\n-\nThe advent of
increased computing capabilities\, along with recent theoretical and numer
ical breakthroughs in the fields of signal processing\, computational harm
onic analysis\, inverse problem solving\, high-dimensional statistics and
convex optimization\, have boosted interactions between low-complexity dat
a models (e.g.\, sparse or low-rank data models) and novel data sensing te
chniques.\nIn a nutshell\, low-complexity data models aim at capturing\, m
odeling and exploiting “just the information you need” in the ubiquito
us data deluge characterizing any scientific or technological achievements
. High dimensional objects can be thus reconstructed using little informat
ion. However\, further developments and novel ideas are still required to
meet new challenges\, especially for efficiently dealing with complex data
structures of "real life" applications and for interconnecting such model
s with other theoretical and applied fields.\nThe iTWIST workshop aims
at fostering collaboration between international scientific teams for deve
loping new theories\, applications and generalizations of low-complexity m
odels. This is why this event emphasizes dissemination of ideas through bo
th specific oral/poster presentations and free discussions.\nFor this edit
ion\, ITWIST will be divided into two parts\, a 2-day doctoral school\, fo
llowed by the actual workshop for three days.\n-\nSubjects :\n- Applicatio
n of low-complexity models in non-convex/non-linear inverse problems such
as phase retrieval\, blind deconvolution\, blind calibration\, or dictiona
ry learning.\n- Machine learning\, deep learning\, compressive statistical
learning and generalized high-dimensional inference of low-complexity sta
tistical models.\n- Optimization methods for recovering low-complexity dat
a representations (e.g.\, low-rank or tensor decomposition).\n- Informatio
n theory\, high-dimensional (convex) geometry and randomness.\n- Novel def
inition of low-complexity models (discrete-valued signals\, co-sparsity\,
mixed/group norm\, model-based\, tensor/manifold models).\n- Sensing and p
rocessing of low-complexity signals on manifolds and graphs.\n- Complexity
and accuracy tradeoffs in numerical methods/optimization.\n- Simulation a
nd/or developments of novel electronic/optical compressive sensors.\n-\nOr
ganizing Committee:\nSandrine Anthoine (Aix-Marseille Université\, France
)\, General Chair\nYannick Boursier (CPPM\, France)\nLaurent Jacques (UCLo
uvain\, Belgium)\nChristine De Mol (Université Libre de Bruxelles\, Belgi
um)\n-\n\nPartners :\n- Aix-Marseille Université (FIR colloques AMU)\n- C
entre International de Rencontres Mathématiques (CIRM)\n- Centre National
de la Recherche Scientifique (CNRS)\n- ED184\n- FNRS\n- GDR MIA\n- GDR MO
A\n- IN2P3\n- Institut de Mathématiques de Marseille (I2M)\n- LabEx Archi
mède\n- LabEx CARMIN\n\n\n-\n\n\n\nSite web du workshop\n\n-
ATTACH;FMTTYPE=image/jpeg:https://test.i2m.univ-amu.fr/wp-content/uploads/
2018/11/image_alea-si-hal-01278102-biphotonic_microscop_views-fig.4-anthoi
ne_and_al-x400.jpg
CATEGORIES:Ecole,Manifestation scientifique,Workshop
LOCATION:CIRM\, Luminy\, 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=CIRM\, Luminy:geo:0,0
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DTSTART:20181028T020000
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