Stable activation-based regression with localizing property

[1]  R.V. Patel,et al.  Stable identification of nonlinear systems using neural networks: theory and experiments , 2006, IEEE/ASME Transactions on Mechatronics.

[2]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[3]  Hui Wang,et al.  Using Radial Basis Function Networks for Function Approximation and Classification , 2012 .

[4]  B. Silverman,et al.  Some Aspects of the Spline Smoothing Approach to Non‐Parametric Regression Curve Fitting , 1985 .

[5]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[6]  G. Wahba,et al.  Hybrid Adaptive Splines , 1997 .

[7]  M. R. Osborne,et al.  On the LASSO and its Dual , 2000 .

[8]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[9]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .

[10]  Seong-Whan Lee,et al.  Penalized B-spline estimator for regression functions using total variation penalty , 2017 .

[11]  Pierre Baldi,et al.  Learning Activation Functions to Improve Deep Neural Networks , 2014, ICLR.

[12]  Alexandre B. Tsybakov,et al.  Introduction to Nonparametric Estimation , 2008, Springer series in statistics.

[13]  Qiyang Zhao,et al.  Suppressing the Unusual: towards Robust CNNs using Symmetric Activation Functions , 2016, ArXiv.

[14]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[15]  Khaldoon A. Bani-Hani,et al.  NONLINEAR STRUCTURAL CONTROL USING NEURAL NETWORKS , 1998 .

[16]  Philip Smith,et al.  Knot selection for least-squares and penalized splines , 2013 .

[17]  E. Mammen,et al.  Optimal spatial adaptation to inhomogeneous smoothness: an approach based on kernel estimates with variable bandwidth selectors , 1997 .