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Dead directions in neural networks can be classified and quantified without traditional alignment methods, revealing deeper insights into network architecture and behavior.
The discovery of "dead directions" reveals a pathway to recover KL divergence rates in overparameterized models, transforming our approach to singular learning in deep networks.
