H. N. Mhaskar

1. Approximation by superposition of sigmoidal and radial basis functions
   1992/09/01 by H.N Mhaskar, H. N. Mhaskar, Charles A. Micchelli +1 · 9 citations
   Computer Science · Mathematics · #Digital Filter Design and Implementation #Mathematical Approximation and Integration #Numerical Methods and Algorithms
2. Learning Functions: When Is Deep Better Than Shallow
   2016/03/03 by H. N. Mhaskar, Mhaskar, Hrushikesh, Qianli Liao +3 · 11 citations
   Computer Science · Engineering · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Sparse and Compressive Sensing Techniques
3. Marcinkiewicz--Zygmund measures on manifolds
   2010/06/26 by Frank Filbir, H. N. Mhaskar, Filbir, F. +1 · 4 citations
   Mathematics · #41a17 #Classical Analysis and ODEs (math.CA) #FOS: Mathematics #Geometric Analysis and Curvature Flows #Geometry and complex manifolds #Morphological variations and asymmetry
4. Theory of Deep Learning III: explaining the non-overfitting puzzle
   2017/12/30 by Tomaso Poggio, Kenji Kawaguchi, Poggio, Tomaso +13 · 11 citations
   Computer Science · Engineering · #Stochastic Gradient Optimization Techniques #Sparse and Compressive Sensing Techniques #Gaussian Processes and Bayesian Inference
5. Data Complexity Estimates for Operator Learning
   2024/05/25 by Nikola Kovachki, Samuel Lanthaler, Kovachki, Nikola B. +3 · 8 citations
   Computer Science · Engineering · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #FOS: Mathematics #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning and Data Classification #Numerical Analysis (math.NA)
6. L^p Bernstein estimates and approximation by spherical basis functions
   2008/10/28 by H. N. Mhaskar, Mhaskar, H. N., F. J. Narcowich +5 · 2 citations
   Mathematics · #41A17 #41A27 #41A63 #42C15 #Classical Analysis and ODEs (math.CA) #FOS: Mathematics #Functional Analysis (math.FA) #Mathematical Analysis and Transform Methods #Mathematical Approximation and Integration #Numerical methods in inverse problems
7. Function approximation by deep networks
   2019/05/30 by H. N. Mhaskar, Mhaskar, H. N., Tomaso Poggio +1 · 2 citations
   Computer Science · #FOS: Computer and information sciences #Face and Expression Recognition #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
8. Representation of functions on big data associated with directed graphs
   2016/07/15 by Charles K. Chui, Chui, Charles K., H. N. Mhaskar +3 · 1 citation
   Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Classical Analysis and ODEs (math.CA) #Complex Network Analysis Techniques #FOS: Mathematics #Graph Theory and Algorithms
9. Deep nets for local manifold learning
   2016/07/24 by Charles K. Chui, H. N. Mhaskar, Chui, Charles K. +1 · 1 citation
   Computer Science · Engineering · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Sparse and Compressive Sensing Techniques
10. A low discrepancy sequence on graphs
    2020/10/08 by Alexander Cloninger, H. N. Mhaskar, Cloninger, A. +1 · 1 citation
    Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Markov Chains and Monte Carlo Methods #Mathematical Approximation and Integration #Probability (math.PR) #advanced mathematical theories
11. Tractability of approximation by general shallow networks
    2023/08/07 by H. N. Mhaskar, Tong Mao, Mhaskar, Hrushikesh +1 · 1 citation
    Neuroscience · #Axon Guidance and Neuronal Signaling #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Numerical Analysis (math.NA)