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Efficient and Modular Implicit Differentiation - Machine Learning Research Paper Explained

Offered By: Yannic Kilcher via YouTube

Tags

Machine Learning Courses Implicit Differentiation Courses Automatic Differentiation Courses Meta-Learning Courses

Course Description

Overview

Explore a comprehensive video explanation of a machine learning research paper on efficient and modular implicit differentiation. Delve into advanced topics like automatic differentiation of inner optimizations, meta-learning, optimization unrolling, and the implicit function theorem. Learn about a unified framework for implicit differentiation of optimization problems that combines autodiff benefits with efficiency and modularity. Discover how this approach can be applied to bi-level optimization problems and sensitivity analysis in molecular dynamics. Follow along with the detailed outline covering key concepts, mathematical foundations, and experimental results presented by the speaker.

Syllabus

- Intro & Overview
- Automatic Differentiation of Inner Optimizations
- Example: Meta-Learning
- Unrolling Optimization
- Unified Framework Overview & Pseudocode
- Implicit Function Theorem
- More Technicalities
- Experiments


Taught by

Yannic Kilcher

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