Geometric Gaussian Mixture Learning of Space Curves

Forschungsthema/Bereich

Artificial Intelligence, Machine Learning, Computational Mathematic

Typ der Abschlussarbeit

Bachelor / Master

Startzeitpunkt

-

Bewerbungsschluss

30.06.2028

Dauer der Arbeit

4 months(BSc) - 6 months(MSc)

Beschreibung

Preface

Sometimes you need a probabilistic representation of a deterministic space curve and for many reasons having them in Gaussian Mixtures is useful. Gaussian mixtures are fitted to data using expectation maximization. The algorithm is not guaranteed to converge to the optimum, is data hungry and quite slow.

Problem

Is it possible to have a Gaussian Mixture representation for a space curve without sampling and coupling it with an expectation maximization step?

Voraussetzung

Voraussetzungen an Studierende

• * Firm grasp over Linear/Multilinear Algebra and Analysis
• * PyTorch or an equivalent NumPy-like framework
• * Some exposition to Functional Analysis and Differential Geometry is advantageous

Studiengangsbereiche

• Ingenieurwissenschaften
  Geodäsie & Geoinformatik
  Informatik
  Mechanical Engineering
  Remote Sensing and Geoinformatics
  Information System Engineering and Management
  Electrical Engineering and Information Technology
  Mechatronics and Information Technology
• Naturwissenschaften und Technik
  Geophysik
  Mathematik
  Computational and Data Sience
  Physics
  Wirtschaftsmathematik

Betreuung

Titel, Vorname, Name

Ali Darijani

Organisationseinheit

* Computer Science(IAR/IES) * Fraunhofer IOSB

E-Mail Adresse

ali.darijani@iosb.fraunhofer.de

Link zur eigenen Homepage/Personenseite

Website

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E-Mail Adresse für die Bewerbung
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an ali.darijani@iosb.fraunhofer.de


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