Hrushikesh N. Mhaskar

54 papers A* 1Journal 53

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	An approximation theory perspective on machine learning. Neural Networks Hrushikesh N. Mhaskar, Efstratios Tsoukanis, Ameya D. Jagtap
2025	J	`jnl`	A signal separation view of classification. CoRR Hrushikesh N. Mhaskar, Ryan O'Dowd
2025	J	`jnl`	Active Learning Classification from a Signal Separation Perspective. CoRR Hrushikesh N. Mhaskar, Ryan O'Dowd, Efstratios Tsoukanis
2025	J	`jnl`	An Approximation Theory Perspective on Machine Learning. CoRR Hrushikesh N. Mhaskar, Efstratios Tsoukanis, Ameya D. Jagtap
2025	J	`jnl`	Approximation by non-symmetric networks for cross-domain learning. Neural Networks Hrushikesh N. Mhaskar
2025	J	`jnl`	Learning on manifolds without manifold learning. Neural Networks Hrushikesh N. Mhaskar, Ryan O'Dowd
2024	J	`jnl`	Inversion of the Laplace Transform of Point Masses. CoRR Michael McKenna, Hrushikesh N. Mhaskar, Richard G. Spencer
2024	J	`jnl`	Learning on manifolds without manifold learning. CoRR Hrushikesh N. Mhaskar, Ryan O'Dowd
2023	J	`jnl`	Approximation by non-symmetric networks for cross-domain learning. CoRR Hrushikesh N. Mhaskar
2023	J	`jnl`	Encoding of data sets and algorithms. CoRR Katarina Doctor, Tong Mao, Hrushikesh N. Mhaskar
2023	J	`jnl`	Numerical solutions to an inverse problem for a non-linear Helmholtz equation. CoRR Quoc Thong Le Gia, Hrushikesh N. Mhaskar
2023	J	`jnl`	Tractability of approximation by general shallow networks. CoRR Hrushikesh N. Mhaskar, Tong Mao
2022	J	`jnl`	A manifold learning approach for gesture recognition from micro-Doppler radar measurements. Neural Networks Eric Mason, Hrushikesh N. Mhaskar, Adam Guo
2022	J	`jnl`	Local approximation of operators. CoRR Hrushikesh N. Mhaskar
2022	J	`jnl`	Theory-Inspired Deep Network for Instantaneous-Frequency Extraction and Subsignals Recovery From Discrete Blind-Source Data. IEEE Trans. Neural Networks Learn. Syst. Ningning Han, Hrushikesh N. Mhaskar, Charles K. Chui
2021	J	`jnl`	A Function Approximation Approach to the Prediction of Blood Glucose Levels. Frontiers Appl. Math. Stat. Hrushikesh N. Mhaskar, Sergei V. Pereverzyev, Maria D. van der Walt
2021	J	`jnl`	A function approximation approach to the prediction of blood glucose levels. CoRR Hrushikesh N. Mhaskar, Sergei V. Pereverzyev, Maria D. van der Walt
2021	J	`jnl`	A manifold learning approach for gesture identification from micro-Doppler radar measurements. CoRR Eric Mason, Hrushikesh N. Mhaskar, Adam Guo
2021	J	`jnl`	Kernel Distance Measures for Time Series, Random Fields and Other Structured Data. Frontiers Appl. Math. Stat. Srinjoy Das, Hrushikesh N. Mhaskar, Alexander Cloninger
2021	J	`jnl`	Kernel distance measures for time series, random fields and other structured data. CoRR Srinjoy Das, Hrushikesh N. Mhaskar, Alexander Cloninger
2020	J	`jnl`	A Witness Function Based Construction of Discriminative Models Using Hermite Polynomials. Frontiers Appl. Math. Stat. Hrushikesh N. Mhaskar, Xiuyuan Cheng, Alexander Cloninger
2020	J	`jnl`	A direct approach for function approximation on data defined manifolds. Neural Networks Hrushikesh N. Mhaskar
2020	J	`jnl`	A low discrepancy sequence on graphs. CoRR Alex Cloninger, Hrushikesh N. Mhaskar
2020	J	`jnl`	An analysis of training and generalization errors in shallow and deep networks. Neural Networks Hrushikesh N. Mhaskar, Tomaso A. Poggio
2020	J	`jnl`	Cautious Active Clustering. CoRR Alexander Cloninger, Hrushikesh N. Mhaskar
2020	J	`jnl`	Dimension independent bounds for general shallow networks. Neural Networks Hrushikesh N. Mhaskar
2020	J	`jnl`	Kernel based analysis of massive data. CoRR Hrushikesh N. Mhaskar
2020	J	`jnl`	Kernel-Based Analysis of Massive Data. Frontiers Appl. Math. Stat. Hrushikesh N. Mhaskar
2020	J	`jnl`	Theory inspired deep network for instantaneous-frequency extraction and signal components recovery from discrete blind-source data. CoRR Charles K. Chui, Ningning Han, Hrushikesh N. Mhaskar
2019	J	`jnl`	A witness function based construction of discriminative models using Hermite polynomials. CoRR Hrushikesh N. Mhaskar, Alex Cloninger, Xiuyuan Cheng
2019	J	`jnl`	Data Based Construction of Kernels for Semi-Supervised Learning With Less Labels. Frontiers Appl. Math. Stat. Hrushikesh N. Mhaskar, Sergei V. Pereverzyev, Vasyl Yu. Semenov, Evgeniya V. Semenova
2019	J	`jnl`	Deep Gaussian networks for function approximation on data defined manifolds. CoRR Hrushikesh N. Mhaskar
2019	J	`jnl`	Dimension independent bounds for general shallow networks. CoRR Hrushikesh N. Mhaskar
2019	J	`jnl`	Function approximation by deep networks. CoRR Hrushikesh N. Mhaskar, Tomaso A. Poggio
2019	J	`jnl`	Function approximation with zonal function networks with activation functions analogous to the rectified linear unit functions. J. Complex. Hrushikesh N. Mhaskar
2019	J	`jnl`	Super-resolution meets machine learning: approximation of measures. CoRR Hrushikesh N. Mhaskar
2018	J	`jnl`	An analysis of training and generalization errors in shallow and deep networks. CoRR Hrushikesh N. Mhaskar, Tomaso A. Poggio
2018	J	`jnl`	Deep Nets for Local Manifold Learning. Frontiers Appl. Math. Stat. Charles K. Chui, Hrushikesh N. Mhaskar
2018	J	`jnl`	Theory of Deep Learning III: explaining the non-overfitting puzzle. CoRR Tomaso A. Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary, Hrushikesh N. Mhaskar
2017	J	`jnl`	A Deep Learning Approach to Diabetic Blood Glucose Prediction. Frontiers Appl. Math. Stat. Hrushikesh N. Mhaskar, Sergei V. Pereverzyev, Maria D. van der Walt
2017	J	`jnl`	A deep learning approach to diabetic blood glucose prediction. CoRR Hrushikesh N. Mhaskar, Sergei V. Pereverzyev, Maria D. van der Walt
2017	J	`jnl`	Function approximation with ReLU-like zonal function networks. CoRR Hrushikesh N. Mhaskar
2017	A*	`conf`	When and Why Are Deep Networks Better Than Shallow Ones? AAAI Hrushikesh N. Mhaskar, Qianli Liao, Tomaso A. Poggio
2017	J	`jnl`	Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. Int. J. Autom. Comput. Tomaso A. Poggio, Hrushikesh N. Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao
2016	J	`jnl`	Deep vs. shallow networks : An approximation theory perspective. CoRR Hrushikesh N. Mhaskar, Tomaso A. Poggio
2016	J	`jnl`	Learning Real and Boolean Functions: When Is Deep Better Than Shallow. CoRR Hrushikesh N. Mhaskar, Qianli Liao, Tomaso A. Poggio
2016	J	`jnl`	Why and When Can Deep - but Not Shallow - Networks Avoid the Curse of Dimensionality: a Review. CoRR Tomaso A. Poggio, Hrushikesh N. Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao
2011	J	`jnl`	A generalized diffusion frame for parsimonious representation of functions on data defined manifolds. Neural Networks Hrushikesh N. Mhaskar
2009	J	`jnl`	Eignets for function approximation on manifolds CoRR Hrushikesh N. Mhaskar
2008	J	`jnl`	Localized Linear Polynomial Operators and Quadrature Formulas on the Sphere. SIAM J. Numer. Anal. Quoc Thong Le Gia, Hrushikesh N. Mhaskar
2007	J	`jnl`	Quadrature in Besov spaces on the Euclidean sphere. J. Complex. Kerstin Hesse, Hrushikesh N. Mhaskar, Ian H. Sloan
2006	J	`jnl`	Matrix-free Interpolation on the Sphere. SIAM J. Numer. Anal. Mahadevan Ganesh, Hrushikesh N. Mhaskar
2006	J	`jnl`	Polynomial operators and local approximation of solutions of pseudo-differential equations on the sphere. Numerische Mathematik Quoc Thong Le Gia, Hrushikesh N. Mhaskar
1996	J	`jnl`	Neural Networks for Optimal Approximation of Smooth and Analytic Functions. Neural Comput. Hrushikesh N. Mhaskar