Discovering Composable Modules in Meta-learning

This lecture explores the concept of discovering composable modules in meta-learning, focusing on the development of a flexible framework for machine learning. The instructor discusses the unprecedented success in AI, efficient learning, broad generalization, and robustness inspired by human learning. Various techniques such as modular meta-learning, model agnostic meta-learning, and Noether Networks are presented, showcasing the advantages and disadvantages of tailoring models during search. The lecture delves into the application of Noether Networks in reducing object morphing artifacts and improving the efficiency of reinforcement learning. Additionally, the importance of encoding inductive biases and the role of symmetries in discovering useful inductive biases are highlighted.