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stat.ML2026
Separation Capacity of Scattering Networks
Konstantin Häberle, Helmut Bölcskei
In this paper, we attempt to enhance the theoretical understanding of convolutional neural networks (CNNs) as feature extractors in classification tasks by analyzing them through t…
stat.ML2026
Covering Numbers for Deep ReLU Networks with Applications to Function Approximation and Nonparametric Regression
Weigutian Ou, Helmut Bölcskei
Covering numbers of (deep) ReLU networks have been used to characterize approximation-theoretic performance, to upper-bound prediction error in nonparametric regression, and to qua…
stat.ML2024
Three Quantization Regimes for ReLU Networks
Weigutian Ou, Philipp Schenkel, Helmut Bölcskei
We establish the fundamental limits in the approximation of Lipschitz functions by deep ReLU neural networks with finite-precision weights. Specifically, three regimes, namely unde…