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
Theoretical Convergence Analysis
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- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Chelsea Finn, Pieter Abbeel, Sergey Levine, 2017 Proceedings of the 34th International Conference on Machine Learning (ICML), Vol. 70 (Proceedings of Machine Learning Research) DOI: 10.5555/3305890.3306001 - Introduces the MAML algorithm and its formulation, laying the groundwork for subsequent theoretical analyses of its convergence properties.
- On First-Order Meta-Learning Algorithms, Alex Nichol, Joshua Achiam, John Schulman, 2018 arXiv preprint arXiv:1803.02999 DOI: 10.48550/arXiv.1803.02999 - Presents the Reptile algorithm, a first-order meta-learning method, and discusses its connection to MAML and multi-task learning, offering insights into its convergence.
- iMAML: Implicit Model-Agnostic Meta-Learning, Aniruddha Rajeswaran, Chelsea Finn, Sanmi Koyejo, Sergey Levine, 2019 Advances in Neural Information Processing Systems (NeurIPS), Vol. 32 (NeurIPS) DOI: 10.5591/978-1-57753-441-2_7 - Introduces iMAML, an implicit differentiation approach to meta-learning, providing theoretical insights into its convergence by avoiding explicit differentiation through inner loop steps.