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
Connections to Hyperparameter Optimization
Was this section helpful?
- 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: 10.5555/3305890.3306016 - Foundational paper introducing Model-Agnostic Meta-Learning (MAML), a gradient-based meta-learning algorithm with a bilevel optimization structure.
- Gradient-Based Hyperparameter Optimization Through Reversible Learning, Dougal Maclaurin, David Duvenaud, and Ryan P. Adams, 2015 Proceedings of the 32nd International Conference on Machine Learning (ICML), Vol. 37 DOI: 10.1137/1.9781611974542.48 - Introduces an early method for efficiently calculating exact gradients of validation performance with respect to hyperparameters, a core concept for gradient-based HPO.
- Automated Machine Learning: Methods, Systems, Challenges, Frank Hutter, Lars Kotthoff, Joaquin Vanschoren, 2019 (Springer) DOI: 10.1007/978-3-030-05318-5 - A comprehensive book covering various aspects of Automated Machine Learning (AutoML), including a dedicated section on hyperparameter optimization (HPO) methods.
- Optimizing Millions of Hyperparameters by Implicit Differentiation, Fabian Pedregosa, 2016 Advances in Neural Information Processing Systems (NeurIPS), Vol. 29 DOI: 10.5555/3045390.3045470 - Explores how implicit differentiation can be applied to optimize a large number of hyperparameters, drawing a parallel to the high-dimensional meta-parameters in meta-learning.