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#learning-dynamics News & Analysis

2 articles tagged with #learning-dynamics. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles
AINeutralarXiv – CS AI · Apr 107/10
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Information as Structural Alignment: A Dynamical Theory of Continual Learning

Researchers introduce the Informational Buildup Framework (IBF), a new approach to continual learning that eliminates catastrophic forgetting by treating information as structural alignment rather than stored parameters. The framework demonstrates superior performance across multiple domains including chess and image classification, achieving near-zero forgetting without requiring raw data replay.

AINeutralarXiv – CS AI · Mar 37/107
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Scaling of learning time for high dimensional inputs

Researchers present theoretical analysis showing that neural network learning times scale supralinearly with input dimensionality, creating fundamental limitations for high-dimensional learning. The study uses Hebbian learning models to demonstrate that higher input dimensions result in smaller gradients and prohibitively long learning times.