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Learning Sparse Boolean Functions: Neural Networks Need a Hierarchical Degree Chain

Offered By: NCCR SwissMAP via YouTube

Tags

Neural Networks Courses Machine Learning Courses Complexity Theory Courses Computational Learning Theory Courses Function Approximation Courses Boolean Circuits Courses

Course Description

Overview

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Explore a thought-provoking lecture on the intricacies of learning sparse Boolean functions, focusing on the necessity of hierarchical degree chains in neural networks. Delve into the research presented by E. Abbé from EPFL as part of the Workshop on Spin Glasses. Gain insights into the complex relationship between sparse Boolean functions and neural network architectures, and understand the implications for machine learning and artificial intelligence. Discover how hierarchical structures in neural networks contribute to their ability to learn and represent sparse Boolean functions effectively.

Syllabus

Learning sparse Boolean functions: neural networks need a hierachical degree chain, E. Abbé (EPFL)


Taught by

NCCR SwissMAP

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