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
Algorithms for Solving Bilevel Problems
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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, Vol. 70 (PMLR) DOI: 10.55989/t2q5 - This foundational paper introduces MAML, a prominent algorithm for meta-learning that exemplifies the inner loop unrolling approach to bilevel optimization.
- Bilevel Optimization for Machine Learning: A Survey, Yifan Lu, Zhichao Huang, Luyao Niu, Weishan Zhang, Xiang Li, Shouyang Wang, Xin Li, Xiaodong Yang, Song Guo, 2020 IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 43 (IEEE) DOI: 10.1109/TPAMI.2020.3006214 - This survey provides a comprehensive overview of bilevel optimization techniques applied to various machine learning problems, including meta-learning, and discusses different algorithmic approaches.
- OptNet: Differentiable Optimization as a Layer in Neural Networks, Brandon Amos, J. Zico Kolter, 2017 Proceedings of the 34th International Conference on Machine Learning, Vol. 70 (PMLR) DOI: 10.55989/v70-amos17a - This paper provides foundational insights into differentiating through optimization problems, a core technique underlying implicit differentiation methods for meta-learning and other machine learning tasks.
- On First-Order Meta-Learning Algorithms, Alex Nichol, Joshua Achiam, John Schulman, 2018 arXiv, Vol. abs/1803.02999 DOI: 10.48550/arXiv.1803.02999 - Introduces Reptile, a simple and efficient first-order meta-learning algorithm that offers an alternative perspective to gradient-based meta-learning by approximating the meta-gradient through repeated SGD steps.