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We rigorously prove that deep Gaussian process priors can outperform Gaussian process priors if the target function has a compositional structure.
Estimation error analysis of deep learning on the regression problem on the variable exponent Besov space
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Wavelets on the interval and fast wavelet transforms
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Minimax theory of image reconstruction
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Rasmussen, C. E., and Williams, C. K. I · 2006
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Castillo, I · 2008
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Variational Bayes under model misspecification
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Frequentist consistency of variational Bayes
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A Bayesian approach to wavelet-based modelling of discontinuous functions applied to inverse problems
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Convergence rates of variational inference in sparse deep learning
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On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces
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Posterior contraction for deep Gaussian process priors, 2021
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The Kolmogorov–Arnold representation theorem revisited
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Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space
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