Geometric Science of Information

Third International Conference, GSI 2017, Paris, France, November 7-9, 2017, Proceedings

  • Frank Nielsen
  • Frédéric Barbaresco

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10589)

Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 10589)

Table of contents

  1. Front Matter
    Pages I-XXV
  2. Statistics on Non-linear Data

    1. Front Matter
      Pages 1-1
    2. Line Kühnel, Stefan Sommer
      Pages 3-11
    3. Benjamin Eltzner, Stephan Huckemann
      Pages 12-19
    4. Maxime Louis, Alexandre Bône, Benjamin Charlier, Stanley Durrleman, The Alzheimer’s Disease Neuroimaging Initiative
      Pages 29-37
    5. Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg
      Pages 38-46
  3. Shape Space

    1. Front Matter
      Pages 47-47
    2. Elena Celledoni, Sølve Eidnes, Markus Eslitzbichler, Alexander Schmeding
      Pages 49-56
    3. Alice Le Brigant, Marc Arnaudon, Frédéric Barbaresco
      Pages 57-64
    4. Kathrin Welker
      Pages 65-72
    5. Pierre Roussillon, Joan Alexis Glaunès
      Pages 73-80
  4. Optimal Transport and Applications: Image Processing

    1. Front Matter
      Pages 81-81
    2. Elsa Cazelles, Jérémie Bigot, Nicolas Papadakis
      Pages 83-90
    3. Giovanni Conforti, Michele Pavon
      Pages 91-99
    4. Bruno Galerne, Arthur Leclaire, Julien Rabin
      Pages 100-108
  5. Optimal Transport and Applications: Signal Processing

    1. Front Matter
      Pages 117-117
    2. Ryo Karakida, Shun-ichi Amari
      Pages 119-126

About these proceedings

Introduction

This book constitutes the refereed proceedings of the Third International Conference on Geometric Science of Information, GSI 2017, held in Paris, France, in November 2017.

The 101 full papers presented were carefully reviewed and selected from 113 submissions and are organized into the following subjects: 

  • Statistics on non-linear data
  • Shape Space
  • Optimal Transport & Applications I (Data Science and Economics)
  • Optimal Transport & Applications II (Signal and Image Processing)
  • Topology and statistical learning
  • Statistical Manifold & Hessian Information Geometry
  • Monotone Embedding in Information Geometry
  • Information Structure in Neuroscience
  • Geometric Robotics & Tracking
  • Geometric Mechanics & Robotics
  • Stochastic Geometric Mechanics & Lie Group Thermodynamics
  • Probability on Riemannian Manifolds
  • Divergence Geometry
  • Geometric Deep Learning
  • First and second-order Optimization on Statistical Manifolds
  • Non-parametric Information Geometry
  • Geometry of quantum states
  • Optimization on Manifold
  • Computational Information Geometry
  • Probability Density Estimation
  • Geometry of Tensor-Valued Data
  • Geometry and Inverse Problems
  • Geometry in Vision, Learning and Dynamical Systems
  • Lie Groups and Wavelets
  • Geometry of metric measure spaces
  • Geometry and Telecom
  • Geodesic Methods with Constraints
  • Applications of Distance Geometry

Keywords

probability Artificial Intelligence signal processing numerical methods image processing probability distributions machine learning fourier transforms

Editors and affiliations

  1. 1.Ecole PolytechniquePalaiseauFrance
  2. 2.Thales Land and Air SystemsLimoursFrance

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-68445-1
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-68444-4
  • Online ISBN 978-3-319-68445-1
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book