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Principled Simplicial Neural Networks for Trajectory Prediction

Offered By: IEEE Signal Processing Society via YouTube

Tags

Neural Networks Courses Data Science Courses Machine Learning Courses Signal Processing Courses Graph Theory Courses Simplicial Complexes Courses

Course Description

Overview

Explore principled simplicial neural networks for trajectory prediction in this comprehensive webinar presented by Santiago Segarra from Rice University. Delve into advanced concepts as part of the Data sciEnce on GrAphS (DEGAS) Webinar Series, organized in collaboration with the IEEE Signal Processing Society Data Science Initiative. Gain valuable insights into cutting-edge techniques for predicting trajectories using simplicial neural networks, and understand their applications in various fields. Learn from an expert in the field as you navigate through this 72-minute presentation, which offers a deep dive into the theoretical foundations and practical implementations of this innovative approach to trajectory prediction.

Syllabus

Principled Simplicial Neural Networks for Trajectory Prediction


Taught by

IEEE Signal Processing Society

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