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Exploring Open-Ended Algorithms: POET

Machine Learning Street Talk (MLST)

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Exploring Symmetries and Inductive Priors in Neural Networks

This chapter examines the interplay between symmetries and inductive priors in neural network architectures, emphasizing their significance for open-ended algorithms. It discusses the challenges of balancing model exploration with the use of priors in evolutionary strategies, particularly in structured environments.

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