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
Efficient Task Sampling and Batching
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- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Chelsea Finn, Pieter Abbeel, and Sergey Levine, 2017 ICML 2017 DOI: 10.48550/arXiv.1703.03400 - This foundational paper introduces MAML, a prominent gradient-based meta-learning algorithm. It outlines the meta-training loop, providing context for discussions on task sampling and batching strategies.
- Gradient-based meta-learning with an adaptive task curriculum, Wei-Ming Li, Yu-Jia Li, Yu-Yang Xia, Yu-Bin Li, and Jia-Xiang Shang, 2020 Neurocomputing, Vol. 383 (Elsevier) DOI: 10.1016/j.neucom.2019.06.079 - This research explores curriculum learning within gradient-based meta-learning, proposing an adaptive method to order tasks based on difficulty. It offers specific strategies for structured task sampling.
- Adaptive Task Selection for Meta-Learning, Pengfei Liu, Xizewen Han, Yichao Wu, Jie Li, and Huadong Ma, 2021 Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35 (AAAI Press) DOI: 10.1609/aaai.v35i10.17187 - This paper investigates adaptive task selection techniques for meta-learning, directly addressing the concepts of hard task mining and diversity-focused sampling to boost efficiency.