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
Implicit MAML (iMAML)
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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: 10.48550/arXiv.1703.03400 - The foundational paper introducing Model-Agnostic Meta-Learning (MAML), which Implicit MAML (iMAML) aims to improve upon.
- Meta-Learning with Implicit Gradients, Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine, 2019 Advances in Neural Information Processing Systems (NeurIPS), Vol. 32 (Neural Information Processing Systems Foundation, Inc. (NeurIPS)) DOI: 10.48550/arXiv.1909.04630 - Introduces Implicit MAML (iMAML) as an efficient alternative to MAML, leveraging implicit differentiation to avoid explicit Hessian computation.
- Optimizing Millions of Variables by Implicit Differentiation, Prashant Donti, Brandon Amos, J. Zico Kolter, 2020 International Conference on Machine Learning (ICML), Vol. 119 (Proceedings of Machine Learning Research) DOI: 10.48550/arXiv.2002.06206 - Provides a framework for scaling implicit differentiation to optimize high-dimensional problems, highly relevant to the computational techniques used in iMAML.
- A Survey of Meta-Learning, Joaquin Vanschoren, 2018 arXiv preprint arXiv:1810.03548 DOI: 10.48550/arXiv.1810.03548 - A comprehensive survey that provides a broad overview of meta-learning techniques, including gradient-based methods and their advancements.