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Found 5 papers, 3 papers with code
A Two-Scale Complexity Measure for Deep Learning Models
We introduce a novel capacity measure 2sED for statistical models based on the effective dimension.
Infinite-width limit of deep linear neural networks
We finally study the continuous-time limit obtained for infinitely wide linear neural networks and show that the linear predictors of the neural network converge at an exponential rate to the minimal $\ell_2$-norm minimizer of the risk.
Effective dimension of machine learning models
Making statements about the performance of trained models on tasks involving new data is one of the primary goals of machine learning, i. e., to understand the generalization power of a model.
The power of quantum neural networks
We show that quantum neural networks are able to achieve a significantly better effective dimension than comparable classical neural networks.
A scale-dependent notion of effective dimension
We introduce a notion of "effective dimension" of a statistical model based on the number of cubes of size $1/\sqrt{n}$ needed to cover the model space when endowed with the Fisher Information Matrix as metric, $n$ being the number of observations.
