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Tongji University, Yuying College of Vocational Technology
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Generalized convexity and smoothness reveal that optimal learning rates in DNN training can be achieved through a unified framework that challenges classical optimization assumptions.
Data fundamentally limits DNN trainability, regardless of model architecture, according to a new model-agnostic lower bound on achievable empirical risk.
