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
Challenges in Applying Meta-Learning to Foundation Models
Was this section helpful?
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Chelsea Finn, Pieter Abbeel, Sergey Levine, 2017 International Conference on Machine Learning (ICML), Vol. 70 (Proceedings of Machine Learning Research (PMLR)) DOI: 10.5555/3305890.3306019 - Introduces Model-Agnostic Meta-Learning (MAML), a fundamental algorithm whose computational demands and optimization needs highlight the difficulties of applying meta-learning to large models.
- A Survey of Meta-Learning for Few-Shot Learning, Haoqian Wu, Yanzhao Xie, Yunong Wu, Jianqiang Wang, 2021 Neurocomputing, Vol. 461 (Elsevier) DOI: 10.1016/j.neucom.2021.08.067 - Provides an overview of meta-learning, discussing various algorithms and their challenges, including the scalability and optimization issues pertinent to foundation models.
- LoRA: Low-Rank Adaptation of Large Language Models, Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, 2021 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2106.09685 - Introduces Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method that addresses computational and memory challenges when adapting large foundation models, offering a practical solution for meta-learning's inner-loop.
- On the Optimization of Deep Learning Models for Few-Shot Learning, Maithra Raghu, Shreya Chaudhuri, Maël Obé, J. P. Lewis, Jonathon Shlens, Chelsea Finn, 2020 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1909.02271 - Investigates the optimization landscape and issues in few-shot learning models, offering insights into the stability and hyperparameter sensitivity encountered when scaling these methods.