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
Scalability Considerations for Foundation Models
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- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Chelsea Finn, Pieter Abbeel, and Sergey Levine, 2017 Proceedings of the 34th International Conference on Machine Learning (ICML), Vol. 70 (PMLR (Proceedings of Machine Learning Research)) DOI: 10.5555/3305890.3306019 - Introduces Model-Agnostic Meta-Learning (MAML), a foundational gradient-based meta-learning algorithm whose computational and memory demands for second-order derivatives are a central challenge for scaling to foundation models.
- On First-Order Meta-Learning Algorithms, Alex Nichol, Joshua Achiam, and John Schulman, 2018 arXiv preprint arXiv:1803.02999 (arXiv) - Presents Reptile, a first-order meta-learning algorithm that offers a more computationally efficient alternative to MAML, making it more practical for large-scale applications, directly addressing scalability.
- LoRA: Low-Rank Adaptation of Large Language Models, Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen, 2021 International Conference on Learning Representations (ICLR 2022) (OpenReview.net) DOI: 10.48550/arXiv.2106.09685 - Introduces Low-Rank Adaptation (LoRA), a prominent parameter-efficient fine-tuning technique that reduces the number of trainable parameters, which is essential for parameter-efficient meta-learning on foundation models.
- ZeRO: Memory Optimizations Toward Training Trillion Parameter Models, Samyam Rajbhandari, Cong Guo, Jeff Rasley, Shaden Smith, and Yuxiong He, 2020 Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC) (IEEE) DOI: 10.1109/SC41405.2020.00078 - Details ZeRO, a memory-efficient optimizer for large-scale distributed training, directly addressing the significant memory footprint and computational demands of foundation models, which is crucial for scalable meta-learning.