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
Open Problems and Research Directions
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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) DOI: 10.5591/978-1-57766-709-0_165 - This foundational paper introduces MAML, outlining the bilevel optimization structure that contributes to significant computational demands, especially for large models, a core open problem discussed.
- Adaptive Rank-tuning for Parameter-Efficient Few-Shot Fine-tuning, Zhao, Xueliang, Fu, Tingchen, Tao, Chongyang, Yan, Rui, 2022 Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics) DOI: 10.18653/v1/2022.emnlp-main.123 - This work addresses the challenge of optimally combining meta-learning with PEFT methods by adaptively tuning PEFT configurations, directly relevant to the section's discussion on hybrid approaches for foundation models.
- Learning to Learn for Global-Scale Robustness, Andrew Apostolopoulos, Apoorva Khetan, and Tal Galanti, 2021 Proceedings of the 38th International Conference on Machine Learning (ICML), Vol. 139 (Proceedings of Machine Learning Research) DOI: 10.5555/3496155.3496229 - This research investigates meta-learning approaches to enhance model robustness against out-of-distribution task shifts, a key challenge for real-world application of meta-learned adaptation strategies.