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Geometric Viewpoints on Learning, Data, and Neural Networks
A small local meeting bringing together researchers in Munich working near geometry, topology, mathematical machine learning, and structured neural architectures.
This workshop explores several geometric viewpoints on machine learning, ranging from topology of data and representations to geometric and algebraic approaches to neural networks and optimization. It is aimed at advanced master students and early PhD students, and emphasizes accessible introductions to active local research directions.
Topics
- Geometry and topology of data
- Geometry of representations and architectures
- Geometry and algebra of optimization and neural networks
Audience
Advanced master’s students and PhD students working near geometry, topology, mathematical ML, graph learning, physics-inspired ML, and adjacent areas.