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Mathematics - Geometry & Topology | Information Geometry

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Information Geometry

Information Geometry

Editor-in-Chief: Shinto Eguchi
Co-Editors: N. Ay; F. Nielsen; J. Zhang

ISSN: 2511-2481 (print version)
ISSN: 2511-249X (electronic version)

Journal no. 41884

Call for Papers

Here is a full list of all current calls for papers.

Special Issue: Information Geometry for Deep Learning 

Submission Deadline: 30th June 2019

Deep learning is a subset of machine learning which is quickly developing in recent years both in terms of methodology and practical applications.
Deep neural networks (DNNs) are artificial neural network architectures with parameter spaces geometrically interpreted as neuromanifolds (with singularities) and learning algorithms visualized as trajectories/flows on neuromanifolds.
The aim of this special issue is to comprise original theoretical/experimental research articles which address the recent developments and research efforts on information-geometric methods in deep learning.
The topics include but are not limited to:
  • Properties and complexity of neural networks/neuromanifolds
  • Geometric dynamic learning with singularities
  • Optimization with natural gradient methods, proximal methods, and other alternative methods.
  • Information geometry of generative models (f-GANs, VAEs, etc)
  • Wasserstein/Fisher-Rao metric spectral properties
  • Information bottleneck of neural networks
  • Neural network simplification and quantization
  • Geometric characterization of robustness and adversarial attacks
Guest Editor:
Frank Nielsen (Sony Computer Science Laboratories, Tokyo, Japan).
For any further queries, please contact Frank Nielsen Frank.Nielsen@acm.org
https://franknielsen.github.io/INGE/IG4DL.html
  • Publication date of the special issue: 2020

Special Issue: Information Geometry and Optimal Transport 

Submisstion Deadline: 20th December 2019

Optimal transport is an interdisciplinary field of mathematics at the intersection of probability, analysis, and geometry. Originally conceived by Monge in 1781 as a problem of finding the most efficient transportation of resources, the modern framework was developed by Kantorovich and others in the early 1900s as a problem of finding optimal coupling between two probability measures characterizing the transported resource. In recent decades, the field of optimal transport has flourished due to its deep connections with many different areas of mathematics and ever expanding applications in other fields.
Connections between the geometry of optimal transport (Wasserstein geometry) and information geometry have also started to emerge. The aim of this special issue is to explore some of these developments and their applications of this promising area of research.
Topics include but are not limited to:
  • 1. Wasserstein-Fisher Rao geometry and entropy-related transportation
  • 2. Displacement interpolation and convexity
  • 3. Regularity theory of optimal transport and its relation to information geometry
  • 4. Talagrand inequalities and the relationship between Wasserstein distances and relative entropy
  • 5. Log-divergences and their applications in economics
  • 6. Wasserstein natural gradient and application to data/image analysis
  • 7. Geometric frameworks to machine learning and computer graphics, etc.
Guest Editor:
Jun Zhang (University of Michigan)
For any further queries, please contact Jun Zhang junz@umich.edu
  • Publication date of the special issue: 2020

Special Issue: Affine Differential Geometry and Hesse Geometry: A Tribute and Memorial to Jean-Louis Koszul 

Submission Deadline: 30th November 2019

Jean-Louis Koszul (January 3, 1921 – January 12, 2018) was a French mathematician with prominent influence to a wide range of mathematical fields. He was a second generation member of Bourbaki, with several notions in geometry and algebra named after him. He made a great contribution to the fundamental theory of Differential Geometry, which is foundation of Information Geometry. The special issue is dedicated to Koszul for the mathematics he developed that bear on information sciences.
Both original contributions and review articles are solicited. Topics include but are not limited to:
  • Affine differential geometry over statistical manifolds
  • Hessian and Kahler geometry
  • Divergence geometry
  • Convex geometry and analysis
  • Differential geometry over homogeneous and symmetric spaces
  • Jordan algebras and graded Lie algebras
  • Pre-Lie algebras and their cohomology
  • Geometric mechanics and Thermodynamics over homogeneous spaces
Guest Editor:
Hideyuki Ishi (Graduate School of Mathematics, Nagoya University)
For any further queries, please contact Hideyuki Ishi hideyuki@math.nagoya-u.ac.jp
  • Publication date of the special issue: 2020

For authors and editors

  • Aims and Scope

    Aims and Scope

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    This journal will publish original work in the emerging interdisciplinary field of information geometry, with both a theoretical and computational emphasis. Information geometry connects various branches of mathematical science in dealing with uncertainty and information based on unifying geometric concepts. Furthermore, it demonstrates the great potential of abstract thinking and corresponding formalisms within many application fields.

    Theoretical topics of interest will include, but are not limited to, the Fisher–Rao metric, the Amari–Chentsov tensor, alpha geometry, dual connections, exponential and mixture geodesics, divergence functions, information and entropy functions, convex analysis, Hessian geometry, information projections, q-statistics and deformed exponential/logarithm, algebraic statistics, optimal transport geometry, and related topics.

    The authors and audience of this journal will be interdisciplinary, coming from the many disciplines that inspire the development of information-geometric methods and benefit from their application, including mathematics, statistics, machine learning, neuroscience, information theory, statistical and quantum physics, control theory and optimization, complex networks and systems, theoretical biology, cognitive science, mathematical finance, and allied disciplines.

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  • Call for Papers: Poster (pdf, 2.1 MB)
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