New Error Bounds for Deep ReLU Networks Using Sparse Grids
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[1] Jie Shen,et al. Sparse Spectral Approximations of High-Dimensional Problems Based on Hyperbolic Cross , 2010, SIAM J. Numer. Anal..
[2] E. M. Wright,et al. Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.
[3] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[4] Allan Pinkus,et al. Approximation theory of the MLP model in neural networks , 1999, Acta Numerica.
[5] E. N. Oberg. The approximate solution of integral equations , 1935 .
[6] G. Lewicki,et al. Approximation by Superpositions of a Sigmoidal Function , 2003 .
[7] M. Irani. Vision Day Schedule Time Speaker and Collaborators Affiliation Title a General Preprocessing Method for Improved Performance of Epipolar Geometry Estimation Algorithms on the Expressive Power of Deep Learning: a Tensor Analysis , 2016 .
[8] Winfried Sickel,et al. Spaces of functions of mixed smoothness and approximation from hyperbolic crosses , 2004, J. Approx. Theory.
[9] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[10] Lorenzo Rosasco,et al. Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review , 2016, International Journal of Automation and Computing.
[11] A. Grossmann,et al. DECOMPOSITION OF HARDY FUNCTIONS INTO SQUARE INTEGRABLE WAVELETS OF CONSTANT SHAPE , 1984 .
[12] Frauke Sprengel,et al. Periodic interpolation and wavelets on sparse grids , 1998, Numerical Algorithms.
[13] Philipp Petersen,et al. Optimal approximation of piecewise smooth functions using deep ReLU neural networks , 2017, Neural Networks.
[14] Matus Telgarsky,et al. Benefits of Depth in Neural Networks , 2016, COLT.
[15] H. Bungartz,et al. Sparse grids , 2004, Acta Numerica.
[16] R. Srikant,et al. Why Deep Neural Networks for Function Approximation? , 2016, ICLR.
[17] H. N. Mhaskar,et al. Neural Networks for Optimal Approximation of Smooth and Analytic Functions , 1996, Neural Computation.
[18] Dmitry Yarotsky,et al. Error bounds for approximations with deep ReLU networks , 2016, Neural Networks.
[19] Ohad Shamir,et al. The Power of Depth for Feedforward Neural Networks , 2015, COLT.