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SUMMARY:GSI2017
DTSTAMP:20170222T025240Z
DTSTART;VALUE=DATE:20171107
DTEND;VALUE=DATE:20171110
URL;VALUE=URI:Page de l'événement
LOCATION:Mines ParisTech\, Paris (France)
DESCRIPTION:As for GSI’13 and GSI’15\, the objective of this SEE Conference GSI’17\, \n
hosted in Paris\, is to bring together pure/applied mathematicians and \n
engineers\, with common interest for Geometric tools and their applications \n
for Information analysis. It emphasizes an active participation of young \n
researchers to discuss emerging areas of collaborative research on \n
“Information Geometry Manifolds and Their Advanced Applications”. Current \n
and ongoing uses of Information Geometry Manifolds in applied mathematics are \n
the following: Advanced Signal/Image/Video Processing\, Complex Data Modeling \n
and Analysis\, Information Ranking and Retrieval\, Coding\, Cognitive Systems\, \n
Optimal Control\, Statistics on Manifolds\, Machine & Deep Learning\, Artificial \n
Intelligence\, Speech/sound recognition\, natural language treatment\, Big Data \n
Analytics\, etc.\, which are also substantially relevant for industry. The \n
Conference will be therefore held in areas of priority/focused themes and \n
topics of mutual interest with the aim to:\n
* Provide an overview on the most recent state-of-the-art\n
* Exchange mathematical information/knowledge/expertise in the area\n
* Identify research areas/applications for future collaboration\n
* Identify academic & industry labs expertise for further collaboration\n
\n
This conference will be an interdisciplinary event and will unify skills from \n
Geometry\, Probability and Information Theory. The conference proceedings are \n
published in Springer's Lecture Note in Computer Science (LNCS) series. \n
Provisional Topics of Special Sessions:\n
* Statistics on non-linear data\n
* Shape Space\n
* Optimal Transport & Applications I (Data Science and Economics)\n
* Optimal Transport & Applications II (Signal and Image Processing)\n
* Topology and statistical learning\n
* Statistical Manifold & Hessian Information Geometry\n
* Monotone Embedding in Information Geometry\n
* Information Structure in Neuroscience\n
* Geometric Robotics & Tracking\n
* Geometric Mechanics & Robotics\n
* Stochastic Geometric Mechanics & Lie Group Thermodynamics\n
* Probability on Riemannian Manifolds\n
* Divergence Geometry\n
* Geometric Deep Learning\n
* First and second-order Optimization on Statistical Manifolds\n
* Non-parametric Information Geometry\n
* Geometry of quantum states\n
* Optimization on Manifold\n
* Computational Information Geometry\n
* Probability Density Estimation\n
* Geometry of Tensor-Valued Data\n
* Geometry and Inverse Problems\n
* Geometry in Vision\, Learning and Dynamical Systems\n
* Lie Groups and Wavelets\n
* Geometry of metric measure spaces\n
* Geometry and Telecom\n
* Geodesic Methods with Constraints\n
* Applications of Distance Geometry\n
\n
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/Faces in the banner\, in order: Euclide\, Thales\, Clairaut\, Legendre\, \n
Poncelet\, Darboux\, Poincaré\, Cartan\, Fréchet\, Libermann\, Leray\, Koszul\, \n
Ferrand\, Souriau\, Balian\, Berger\, Choquet-Bruhat\, Gromov/ /Music is from \n
Pascal Dusapin [1] (born 29 May 1955) is a contemporary French composer born \n
in Nancy\, France. His music is marked by its microtonality\, tension\, and \n
energy. A pupil of Iannis Xenakis and Franco Donatoniand an admirer \n
of Varèse\, Dusapin studied at the University of Paris I and Paris \n
VIII during the 1970s. His music is full of "romantic constraint"\, and he \n
rejects the use of electronics\, percussion other than timpani\, and\, up until \n
the late 1990s\, piano. His melodies have a vocal quality\, even in purely \n
instrumental works. Dusapin has composed solo\, chamber\, orchestral\, vocal\, \n
and choral works\, as well as several operas\, and has been honored with \n
numerous prizes and awards./ / / *******************************\n
Page de l'événement [2]\n
\n
[1] https://en.wikipedia.org/wiki/Pascal_Dusapin\n
[2] https://www.see.asso.fr/manifestation/17408_gsi2017
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