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
Memory Optimization Techniques
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- Training Deep Nets with Sublinear Memory Cost, Tianqi Chen, Bing Xu, Chiyuan Zhang, Carlos Guestrin, 2016 arXiv preprint arXiv:1604.06174 (arXiv) DOI: 10.48550/arXiv.1604.06174 - A foundational paper that introduces gradient checkpointing, a technique for reducing memory consumption during deep neural network training by recomputing intermediate activations instead of storing them.
- Mixed-Precision Training, Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, Hao Wu, 2018 ICLR 2018 DOI: 10.48550/arXiv.1710.03740 - The seminal paper that details the methodology of mixed-precision training, including the use of 16-bit floating-point numbers and loss scaling to improve training speed and reduce memory usage.
- Automatic Mixed Precision package - torch.cuda.amp, PyTorch Contributors, 2024 (PyTorch) - Official PyTorch documentation providing practical guidance and examples for implementing mixed-precision training using the
torch.cuda.amppackage, including details onGradScalerfor loss scaling. - Adafactor: Adaptive Learning Rates with Sublinear Memory Cost, Noam Shazeer, Mitchell Stern, 2018 Proceedings of the 35th International Conference on Machine Learning (ICML), Vol. 80 (PMLR) DOI: 10.5555/3295304.3295415 - Introduces Adafactor, an adaptive learning rate optimizer designed to significantly reduce memory consumption for optimizer states, making it suitable for training very large models.
- 8-bit Optimizers via Block-wise Quantization, Tim Dettmers, Mike Lewis, Sam Shleifer, Luke Zettlemoyer, 2021 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2110.02861 - Presents a method for quantizing optimizer states to 8-bit precision, which substantially decreases the memory footprint of optimizers like Adam while preserving their performance characteristics.