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
Information Theoretic Perspectives
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- The Information Bottleneck Method, Naftali Tishby, Fernando C. Pereira, William Bialek, 1999 Advances in Neural Information Processing Systems (MIT Press) DOI: 10.48550/arXiv.physics/0004057 - Foundational paper introducing the Information Bottleneck principle for data compression and representation learning.
- Deep Variational Information Bottleneck, Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy, 2017 International Conference on Learning Representations (ICLR) DOI: 10.1109/ICLR.2017.00001 - Introduces the Variational Information Bottleneck (VIB) to practically apply the IB principle to deep neural networks, enabling tractable optimization.
- Meta-Learning with the Information Bottleneck, Jeffrey Roth, Cédric Cremer, Louis T. Ewe, Giulio R. S. Ferrari, Alexander Alemi, Naftali Tishby, 2020 International Conference on Learning Representations (ICLR) - Explores how the Information Bottleneck principle can be applied to meta-learning to improve generalization by learning compressed, task-agnostic representations.