<ul><li>Understanding and controlling the informational complexity of neural networks is crucial in machine learning, impacting generalization, optimization, and model capacity.</li><li>A new approach using algorithmic information theory, focusing on Binarized Neural Networks (BNNs), is proposed to capture algorithmic regularities in network structure.</li><li>The Block Decomposition Method (BDM), based on algorithmic probability, is applied to BNNs and shows better tracking of structural changes during training compared to entropy-based approaches, correlating more strongly with training loss.</li><li>This shift towards algorithmic information theory offers insights into learning dynamics, viewing training as algorithmic compression and providing a foundation for complexity-aware learning and regularization.</li></ul>

Evaluating Training in Binarized Neural Networks Through the Lens of Algorithmic Information Theory

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