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IMAML- Meta-Learning with Implicit Gradients

Offered By: Yannic Kilcher via YouTube

Tags

Machine Learning Courses Few-shot Learning Courses Meta-Learning Courses

Course Description

Overview

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.

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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