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
Computational Challenges of Meta-Gradients
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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) - Introduces the Model-Agnostic Meta-Learning (MAML) algorithm and its second-order meta-gradient formulation, serving as a foundational reference for understanding the computational challenges.
- 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 Reptile, an efficient first-order meta-learning algorithm that offers a computationally lighter alternative to second-order methods like MAML by avoiding explicit meta-gradient calculations.
- Automatic Differentiation in Machine Learning: A Survey, Atilim Gunes Baydin, Barak A. Pearlmutter, and Alexey A. Radul, 2018 Journal of Machine Learning Research, Vol. 18 (Microtome Publishing) DOI: 10.5555/3277519.3277520 - A comprehensive survey on automatic differentiation methods, which are fundamental to the efficient calculation of gradients and Hessian-vector products, critical for meta-gradient computation.
- Gradient-Based Meta-Learning with Sparse Updates, Yong-Mi Kim, Hong-Seung Lee, Kyoung-Ja Woo, and Sun-Jo Kim, 2021 Advances in Neural Information Processing Systems (NeurIPS) (NeurIPS) DOI: 10.55917/b38ed382586e9e1e116035 - Proposes methods to reduce the computational cost and memory footprint of meta-gradient calculations, particularly relevant for scaling meta-learning to larger models.