This course provides an in-depth examination of meta-learning principles and their application to the few-shot adaptation of large-scale foundation models. It covers advanced algorithms, theoretical underpinnings, and practical implementation strategies for efficiently adapting models like LLMs and vision transformers to new tasks with minimal data. Study gradient-based, metric-based, and optimization-based meta-learning approaches, alongside specialized adaptation techniques relevant to foundation models.
Prerequisites Strong ML & Deep Learning
Level:
Advanced Meta-Learning Algorithms
Implement and analyze sophisticated gradient-based, metric-based, and optimization-based meta-learning algorithms.
Foundation Model Adaptation
Design and apply effective few-shot adaptation strategies specifically for large foundation models.
Scalability Techniques
Address computational and memory challenges when applying meta-learning to large-scale models.
Theoretical Understanding
Grasp the theoretical guarantees and limitations of different meta-learning approaches in the context of foundation models.
Parameter-Efficient Methods
Analyze and compare meta-learning with parameter-efficient fine-tuning (PEFT) techniques for few-shot adaptation.
Implementation Expertise
Gain practical experience implementing meta-learning adaptation pipelines for foundation models.