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
Taxonomy of Meta-Learning Approaches
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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 DOI: 10.48550/arXiv.1703.03400 - Introduces Model-Agnostic Meta-Learning (MAML), a foundational gradient-based meta-learning algorithm.
- Matching Networks for One Shot Learning, Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra, 2016 Advances in Neural Information Processing Systems (NeurIPS), Vol. 29 DOI: 10.48550/arXiv.1606.04080 - Introduces Matching Networks, an attention-based approach for few-shot learning by comparing query examples to support set examples.
- Learning to learn by gradient descent by gradient descent, Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas, 2016 Advances in Neural Information Processing Systems (NeurIPS), Vol. 29 DOI: 10.48550/arXiv.1606.04474 - Proposes an optimization-based meta-learning approach where an LSTM meta-learner learns an optimization algorithm for a base model.
- On First-Order Meta-Learning Algorithms, Alex Nichol, Joshua Achiam, and John Schulman, 2018 arXiv preprint arXiv:1803.02999 DOI: 10.48550/arXiv.1803.02999 - Describes Reptile, a simple and scalable first-order meta-learning algorithm related to Model-Agnostic Meta-Learning.