Explore a detailed video explanation of the iMAML (Implicit Model-Agnostic Meta-Learning) paper, which presents an innovative approach to gradient-based meta-learning. Learn how this method circumvents the computational challenges of full backpropagation through inner optimization procedures by cleverly introducing a quadratic regularizer. Dive into key concepts including meta-learning fundamentals, the differences between MAML and iMAML, problem formulation, proximal regularization, and the derivation of implicit gradients. Gain insights into the intuition behind this approach, understand the full algorithm, and examine experimental results. This comprehensive breakdown covers the paper's abstract, authors, and provides links to additional resources for further study.
Overview
Syllabus
- Intro
- What is Meta-Learning?
- MAML vs iMAML
- Problem Formulation
- Proximal Regularization
- Derivation of the Implicit Gradient
- Intuition why this works
- Full Algorithm
- Experiments
Taught by
Yannic Kilcher
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