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Meta-Learning and Few-Shot Adaptation in Foundation Models

Chapter 1: Foundations of Meta-Learning Revisited

The Meta-Learning Problem Formulation

Taxonomy of Meta-Learning Approaches

Challenges in Applying Meta-Learning to Foundation Models

Evaluation Protocols for Few-Shot Learning

Chapter 2: Advanced Gradient-Based Meta-Learning

Model-Agnostic Meta-Learning (MAML)

First-Order MAML (FOMAML) and Reptile

Implicit MAML (iMAML)

Addressing Stability and Gradient Variance

Scalability Considerations for Foundation Models

Hands-on Practical: Implementing FOMAML for Model Adaptation

Chapter 3: Advanced Metric-Based Meta-Learning

Prototypical Networks Revisited

Relation Networks for Few-Shot Learning

Matching Networks with Attention

Deep Metric Learning Techniques

Adapting Metric Learning for High-Dimensional Embeddings

Practice: Implementing Prototypical Networks with Foundation Model Embeddings

Chapter 4: Optimization Perspectives on Meta-Learning

Meta-Learning as Bilevel Optimization

Algorithms for Solving Bilevel Problems

Connections to Hyperparameter Optimization

Meta-Learning Initialization Strategies

Theoretical Convergence Analysis

Chapter 5: Few-Shot Adaptation Strategies for Foundation Models

Parameter-Efficient Fine-Tuning (PEFT) Overview

Adapter Modules for Foundation Models

Low-Rank Adaptation (LoRA)

Prompt Tuning and Prefix Tuning

Comparing PEFT and Meta-Learning Approaches

Hybrid Adaptation Strategies

Hands-on Practical: Adapting a Foundation Model using LoRA

Chapter 6: Scaling Meta-Learning Implementations

Computational Challenges of Meta-Gradients

Memory Optimization Techniques

Distributed Meta-Learning Strategies

Efficient Task Sampling and Batching

Approximation Methods for Scalability

Benchmarking Scalable Implementations

Chapter 7: Advanced Topics and Theoretical Considerations

Bayesian Meta-Learning Approaches

Continual Meta-Learning

Meta-Learning for Reinforcement Learning

Generalization Bounds in Meta-Learning

Information Theoretic Perspectives

Open Problems and Research Directions

Meta-Learning and Few-Shot Adaptation in Foundation Models

Prerequisites: Strong ML & Deep Learning

Level:

Expert

What You'll Learn

Advanced Meta-Learning Algorithms
Implement and analyze sophisticated gradient-based, metric-based, and optimization-based meta-learning algorithms.
Foundation Model Adaptation
Design and apply effective few-shot adaptation strategies specifically for large foundation models.
Scalability Techniques
Address computational and memory challenges when applying meta-learning to large-scale models.
Theoretical Understanding
Grasp the theoretical guarantees and limitations of different meta-learning approaches in the context of foundation models.
Parameter-Efficient Methods
Analyze and compare meta-learning with parameter-efficient fine-tuning (PEFT) techniques for few-shot adaptation.
Implementation Expertise
Gain practical experience implementing meta-learning adaptation pipelines for foundation models.

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