How to Choose an Activation Function. - Hrushikesh Narhar Mhaskar; Charles A. Micchelli (NIPS, 1993)

Approximation properties of a multilayered feedforward artificial neural network. - Hrushikesh Narhar Mhaskar (Adv. Comput. Math., 1993)

Dimension-independent bounds on the degree of approximation by neural networks. - Hrushikesh Narhar Mhaskar; Charles A. Micchelli (IBM J. Res. Dev., 1994)

Introduction to the theory of weighted polynomial approximation. - Hrushikesh Narhar Mhaskar (World Scientific, 1996)

Limitations of the approximation capabilities of neural networks with one hidden layer. - Charles K. Chui; Xin Li 019; Hrushikesh Narhar Mhaskar (Adv. Comput. Math., 1996)

Neural Networks for Optimal Approximation of Smooth and Analytic Functions. - Hrushikesh N. Mhaskar (Neural Comput., 1996)

Neural networks and approximation theory. - Hrushikesh Narhar Mhaskar (Neural Networks, 1996)

Neural Networks for Functional Approximation and System Identification. - Hrushikesh Narhar Mhaskar; Nahmwoo Hahm (Neural Comput., 1997)

Approximation properties of zonal function networks using scattered data on the sphere. - Hrushikesh Narhar Mhaskar; Francis J. Narcowich; Joseph D. Ward (Adv. Comput. Math., 1999)

Polynomial frames on the sphere. - Hrushikesh Narhar Mhaskar; Francis J. Narcowich; Jürgen Prestin; Joseph D. Ward (Adv. Comput. Math., 2000)

On the detection of singularities of a periodic function. - Hrushikesh Narhar Mhaskar; Jürgen Prestin (Adv. Comput. Math., 2000)

Spherical Marcinkiewicz-Zygmund inequalities and positive quadrature. - Hrushikesh Narhar Mhaskar; Francis J. Narcowich; Joseph D. Ward (Math. Comput., 2001)

On the Representation of Band Limited Functions Using Finitely Many Bits. - Hrushikesh Narhar Mhaskar (J. Complex., 2002)

Corrigendum to "Spherical Marcinkiewicz-Zygmund inequalities and positive quadrature''. - Hrushikesh Narhar Mhaskar; Francis J. Narcowich; Joseph D. Ward (Math. Comput., 2002)

Zonal function network frames on the sphere. - Hrushikesh Narhar Mhaskar; Francis J. Narcowich; Joseph D. Ward (Neural Networks, 2003)

On the Representation of Band-Dominant Functions on the Sphere Using Finitely Many Bits. - Hrushikesh Narhar Mhaskar; Francis J. Narcowich; Joseph D. Ward (Adv. Comput. Math., 2004)

A tribute to Géza Freud. - Hrushikesh Narhar Mhaskar (J. Approx. Theory, 2004)

Polynomial operators and local smoothness classes on the unit interval. - Hrushikesh Narhar Mhaskar (J. Approx. Theory, 2004)

On the tractability of multivariate integration and approximation by neural networks. - Hrushikesh Narhar Mhaskar (J. Complex., 2004)

Local quadrature formulas on the sphere. - Hrushikesh Narhar Mhaskar (J. Complex., 2004)

When is approximation by Gaussian networks necessarily a linear process? - Hrushikesh Narhar Mhaskar (Neural Networks, 2004)

Weighted quadrature formulas and approximation by zonal function networks on the sphere. - Hrushikesh Narhar Mhaskar (J. Complex., 2006)

Polynomial operators and local approximation of solutions of pseudo-differential equations on the sphere. - Quoc Thong Le Gia; Hrushikesh N. Mhaskar (Numerische Mathematik, 2006)

Matrix-free Interpolation on the Sphere. - Mahadevan Ganesh; Hrushikesh N. Mhaskar (SIAM J. Numer. Anal., 2006)

Frontiers in Interpolation and Approximation. - Narendra Kumar Govil; Hrushikesh Narhar Mhaskar; Ram N. Mohapatra; Zuhair Nashed; József Szabados (Chapman & Hall, 2007)

Quadrature in Besov spaces on the Euclidean sphere. - Kerstin Hesse; Hrushikesh N. Mhaskar; Ian H. Sloan (J. Complex., 2007)

Localized Linear Polynomial Operators and Quadrature Formulas on the Sphere. - Quoc Thong Le Gia; Hrushikesh N. Mhaskar (SIAM J. Numer. Anal., 2008)

Eignets for function approximation on manifolds - Hrushikesh N. Mhaskar (CoRR, 2009)

Preface part 1. - Charles K. Chui; Hrushikesh Narhar Mhaskar (J. Approx. Theory, 2009)

Preface part 2. - Charles K. Chui; Hrushikesh Narhar Mhaskar (J. Approx. Theory, 2009)

Preface part 3. - Charles K. Chui; Hrushikesh Narhar Mhaskar (J. Approx. Theory, 2009)

Preface part 4. - Charles K. Chui; Hrushikesh Narhar Mhaskar (J. Approx. Theory, 2009)

On a filter for exponentially localized kernels based on Jacobi polynomials. - Frank Filbir; Hrushikesh Narhar Mhaskar; Jürgen Prestin (J. Approx. Theory, 2009)

Lp Bernstein estimates and approximation by spherical basis functions. - Hrushikesh Narhar Mhaskar; Francis J. Narcowich; Jürgen Prestin; Joseph D. Ward (Math. Comput., 2010)

Marcinkiewicz-Zygmund measures on manifolds. - Frank Filbir; Hrushikesh Narhar Mhaskar (J. Complex., 2011)

A generalized diffusion frame for parsimonious representation of functions on data defined manifolds. - Hrushikesh N. Mhaskar (Neural Networks, 2011)

Higher Order Numerical Discretization Methods with Sobolev Norm Minimization. - Shiv Chandrasekaran; K. R. Jayaraman; Ming Gu 002; Hrushikesh Narhar Mhaskar; J. Mofftt (ICCS, 2011)

Higher order numerical discretizations for exterior and biharmonic type PDEs. - Karthik Jayaraman Raghuram; Shivkumar Chandrasekaran; Joseph Moffitt; Ming Gu 002; Hrushikesh Narhar Mhaskar (J. Comput. Appl. Math., 2012)

Locally Learning Biomedical Data Using Diffusion Frames. - Martin Ehler; Frank Filbir; Hrushikesh Narhar Mhaskar (J. Comput. Biol., 2012)

Filtered Legendre expansion method for numerical differentiation at the boundary point with application to blood glucose predictions. - Hrushikesh Narhar Mhaskar; Valeriya Naumova; Sergei V. Pereverzyev (Appl. Math. Comput., 2013)

Minimum Sobolev norm interpolation with trigonometric polynomials on the torus. - Shivkumar Chandrasekaran; Karthik Jayaraman Jayaraman; Hrushikesh Narhar Mhaskar (J. Comput. Phys., 2013)

Smooth function extension based on high dimensional unstructured data. - Charles K. Chui; Hrushikesh Narhar Mhaskar (Math. Comput., 2014)

A minimum Sobolev norm technique for the numerical discretization of PDEs. - Shivkumar Chandrasekaran; Hrushikesh Narhar Mhaskar (J. Comput. Phys., 2015)

Learning Real and Boolean Functions: When Is Deep Better Than Shallow. - Hrushikesh N. Mhaskar; Qianli Liao; Tomaso A. Poggio (CoRR, 2016)

Deep vs. shallow networks : An approximation theory perspective. - Hrushikesh N. Mhaskar; Tomaso A. Poggio (CoRR, 2016)

When and Why Are Deep Networks Better Than Shallow Ones? - Hrushikesh N. Mhaskar; Qianli Liao; Tomaso A. Poggio (AAAI, 2017)

A Fourier-invariant method for locating point-masses and computing their attributes. - Charles K. Chui; Hrushikesh Narhar Mhaskar (CoRR, 2017)

A unified method for super-resolution recovery and real exponential-sum separation. - Charles K. Chui; Hrushikesh Narhar Mhaskar (CoRR, 2017)

Function approximation with ReLU-like zonal function networks. - Hrushikesh N. Mhaskar (CoRR, 2017)

A Deep Learning Approach to Diabetic Blood Glucose Prediction. - Hrushikesh N. Mhaskar; Sergei V. Pereverzyev; Maria D. van der Walt (Frontiers Appl. Math. Stat., 2017)

Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. - Tomaso A. Poggio; Hrushikesh N. Mhaskar; Lorenzo Rosasco; Brando Miranda; Qianli Liao (Int. J. Autom. Comput., 2017)

Theory of Deep Learning III: explaining the non-overfitting puzzle. - Tomaso A. Poggio; Kenji Kawaguchi; Qianli Liao; Brando Miranda; Lorenzo Rosasco; Xavier Boix; Jack Hidary; Hrushikesh N. Mhaskar (CoRR, 2018)

Deep Algorithms: designs for networks. - Abhejit Rajagopal; Shivkumar Chandrasekaran; Hrushikesh Narhar Mhaskar (CoRR, 2018)

Deep Nets for Local Manifold Learning. - Charles K. Chui; Hrushikesh N. Mhaskar (Frontiers Appl. Math. Stat., 2018)

Minimum Sobolev norm interpolation of scattered derivative data. - Shivkumar Chandrasekaran; C. H. Gorman; Hrushikesh Narhar Mhaskar (J. Comput. Phys., 2018)

Function approximation by deep networks. - Hrushikesh N. Mhaskar; Tomaso A. Poggio (CoRR, 2019)

Super-resolution meets machine learning: approximation of measures. - Hrushikesh N. Mhaskar (CoRR, 2019)

Deep Gaussian networks for function approximation on data defined manifolds. - Hrushikesh N. Mhaskar (CoRR, 2019)

Data Based Construction of Kernels for Semi-Supervised Learning With Less Labels. - Hrushikesh N. Mhaskar; Sergei V. Pereverzyev; Vasyl Yu. Semenov; Evgeniya V. Semenova (Frontiers Appl. Math. Stat., 2019)

Function approximation with zonal function networks with activation functions analogous to the rectified linear unit functions. - Hrushikesh N. Mhaskar (J. Complex., 2019)

Theory inspired deep network for instantaneous-frequency extraction and signal components recovery from discrete blind-source data. - Charles K. Chui; Ningning Han; Hrushikesh N. Mhaskar (CoRR, 2020)

Cautious Active Clustering. - Alexander Cloninger; Hrushikesh N. Mhaskar (CoRR, 2020)

A low discrepancy sequence on graphs. - Alex Cloninger; Hrushikesh N. Mhaskar (CoRR, 2020)

Kernel-Based Analysis of Massive Data. - Hrushikesh N. Mhaskar (Frontiers Appl. Math. Stat., 2020)

A Witness Function Based Construction of Discriminative Models Using Hermite Polynomials. - Hrushikesh N. Mhaskar; Xiuyuan Cheng; Alexander Cloninger (Frontiers Appl. Math. Stat., 2020)

Dimension independent bounds for general shallow networks. - Hrushikesh N. Mhaskar (Neural Networks, 2020)

A direct approach for function approximation on data defined manifolds. - Hrushikesh N. Mhaskar (Neural Networks, 2020)

An analysis of training and generalization errors in shallow and deep networks. - Hrushikesh N. Mhaskar; Tomaso A. Poggio (Neural Networks, 2020)

A manifold learning approach for gesture identification from micro-Doppler radar measurements. - Eric Mason; Hrushikesh N. Mhaskar; Adam Guo (CoRR, 2021)

Kernel Distance Measures for Time Series, Random Fields and Other Structured Data. - Srinjoy Das; Hrushikesh N. Mhaskar; Alexander Cloninger (Frontiers Appl. Math. Stat., 2021)

A Function Approximation Approach to the Prediction of Blood Glucose Levels. - Hrushikesh N. Mhaskar; Sergei V. Pereverzyev; Maria D. van der Walt (Frontiers Appl. Math. Stat., 2021)

Local approximation of operators. - Hrushikesh N. Mhaskar (CoRR, 2022)

A manifold learning approach for gesture recognition from micro-Doppler radar measurements. - Eric Mason; Hrushikesh N. Mhaskar; Adam Guo (Neural Networks, 2022)

Theory-Inspired Deep Network for Instantaneous-Frequency Extraction and Subsignals Recovery From Discrete Blind-Source Data. - Ningning Han; Hrushikesh N. Mhaskar; Charles K. Chui (IEEE Trans. Neural Networks Learn. Syst., 2022)

Local transfer learning from one data space to another. - Hrushikesh Narhar Mhaskar; Ryan O'Dowd (CoRR, 2023)

Numerical solutions to an inverse problem for a non-linear Helmholtz equation. - Quoc Thong Le Gia; Hrushikesh N. Mhaskar (CoRR, 2023)

Encoding of data sets and algorithms. - Katarina Doctor; Tong Mao; Hrushikesh N. Mhaskar (CoRR, 2023)

Tractability of approximation by general shallow networks. - Hrushikesh N. Mhaskar; Tong Mao (CoRR, 2023)

Inversion of the Laplace Transform of Point Masses. - Michael McKenna; Hrushikesh N. Mhaskar; Richard G. Spencer 001 (CoRR, 2024)

Approximation by non-symmetric networks for cross-domain learning. - Hrushikesh N. Mhaskar (Neural Networks, 2025)

Learning on manifolds without manifold learning. - Hrushikesh N. Mhaskar; Ryan O'Dowd (Neural Networks, 2025)

Active Learning Classification from a Signal Separation Perspective. - Hrushikesh N. Mhaskar; Ryan O'Dowd; Efstratios Tsoukanis (CoRR, 2025)

An Approximation Theory Perspective on Machine Learning. - Hrushikesh N. Mhaskar; Efstratios Tsoukanis; Ameya D. Jagtap (CoRR, 2025)