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[Theoretical Background] Convex Optimization 1
This year, I took a class on Optimization for the first time. In the Deep Learning field, Optimization is a foundational discipline for mathematically verifying whether convergence occurs and, if so, how quickly it occurs. Although I have previously posted about Meta-Learning and Generative Models, and announced that I would post about Foundation Models in the future, I also thought it would be good to share some concepts based on mathematics. So, I plan to post mainly about what I have learned in my graduate school classes. Since I have organized these materials, I plan to post them periodically without making excuses.
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[Conceptual Background] Meta Learning (2) - Approaches
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[Conceptual Background] Meta Learning (1) - Few-Shot Learning
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[Paper Review] Sharp-MAML
For my first blog post, I would like to write about Sharp-MAML, which combines SAM, a topic that has attracted considerable attention in generalization research since 2020, with MAML, one of the pioneering Meta-Learning algorithms. After briefly introducing SAM and MAML, I will explain what contribution comes from combining the two.