Nested Learning: The Illusion of Deep Learning Architectures
Analysis
This article introduces Nested Learning (NL) as a new paradigm for machine learning, challenging the conventional understanding of deep learning. It proposes that existing deep learning methods compress their context flow, and in-context learning arises naturally in large models. The paper highlights three core contributions: expressive optimizers, a self-modifying learning module, and a focus on continual learning. The article's core argument is that NL offers a more expressive and potentially more effective approach to machine learning, particularly in areas like continual learning.
Key Takeaways
- •Nested Learning (NL) is presented as a new paradigm for machine learning.
- •NL views deep learning as compressing context flow.
- •The paper highlights expressive optimizers, self-modifying learning modules, and continual learning.
- •NL aims to improve in-context and continual learning capabilities.
“NL suggests a philosophy to design more expressive learning algorithms with more levels, resulting in higher-order in-context learning and potentially unlocking effective continual learning capabilities.”