What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Real number
- Algebra
His primary areas of investigation include Mathematical analysis, Combinatorics, Function, Artificial neural network and Discrete mathematics.
His research integrates issues of Universal approximation theorem, Quadrature and Continuous function in his study of Mathematical analysis.
His Combinatorics research incorporates themes from Riemannian manifold, Boundary and Exponential function.
The Function study combines topics in areas such as Activation function and Radial basis function network.
His Artificial neural network research is multidisciplinary, incorporating elements of Deep learning, Convolutional neural network and Theoretical computer science.
His work deals with themes such as Curse of dimensionality and Special case, which intersect with Convolutional neural network.
His most cited work include:
- Why and When Can Deep – but Not Shallow – Networks Avoid the Curse of Dimensionality: a Review (250 citations)
- Why and When Can Deep – but Not Shallow – Networks Avoid the Curse of Dimensionality: a Review (250 citations)
- Neural networks for optimal approximation of smooth and analytic functions (247 citations)
What are the main themes of his work throughout his whole career to date?
Hrushikesh N. Mhaskar mainly investigates Mathematical analysis, Function, Combinatorics, Discrete mathematics and Function approximation.
He has researched Mathematical analysis in several fields, including Applied mathematics, Quadrature and Pure mathematics.
His Function study also includes
- Class most often made with reference to Artificial neural network,
- Activation function together with Radial basis function network.
In his research, Boundary is intimately related to Riemannian manifold, which falls under the overarching field of Combinatorics.
The study incorporates disciplines such as Nonlinear dimensionality reduction, Theoretical computer science, Convolutional neural network and Curse of dimensionality in addition to Function approximation.
His Theoretical computer science research integrates issues from Deep learning, Cluster analysis and Artificial intelligence.
He most often published in these fields:
- Mathematical analysis (34.30%)
- Function (29.47%)
- Combinatorics (20.29%)
What were the highlights of his more recent work (between 2016-2021)?
- Function (29.47%)
- Function approximation (20.29%)
- Artificial intelligence (12.08%)
In recent papers he was focusing on the following fields of study:
Hrushikesh N. Mhaskar spends much of his time researching Function, Function approximation, Artificial intelligence, Curse of dimensionality and Algorithm.
Hrushikesh N. Mhaskar has included themes like Smoothness, Smoothness, Discrete mathematics and Metric in his Function study.
Hrushikesh N. Mhaskar interconnects Nonlinear dimensionality reduction, Manifold, Activation function and Propagation of uncertainty in the investigation of issues within Function approximation.
His Artificial intelligence research focuses on Machine learning and how it connects with Orthonormal basis, Bounded function and Continuum.
His Curse of dimensionality research is multidisciplinary, incorporating perspectives in Artificial neural network and Directed acyclic graph.
His Algorithm research is multidisciplinary, relying on both Exponential function, Exponential sum, Simple and Inverse problem.
Between 2016 and 2021, his most popular works were:
- Why and When Can Deep – but Not Shallow – Networks Avoid the Curse of Dimensionality: a Review (250 citations)
- Why and When Can Deep – but Not Shallow – Networks Avoid the Curse of Dimensionality: a Review (250 citations)
- When and Why Are Deep Networks Better Than Shallow Ones (74 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Algebra
- Real number
Hrushikesh N. Mhaskar focuses on Representation, Function, Artificial intelligence, Deep learning and Algebra.
His Representation study combines topics in areas such as Mathematical proof, Smoothness, Mathematical analysis, Real line and Pure mathematics.
His research in Function intersects with topics in Discrete mathematics, Structure, Hermite functions and Nonlinear dimensionality reduction.
Many of his research projects under Artificial intelligence are closely connected to Continuous glucose monitoring and Novelty with Continuous glucose monitoring and Novelty, tying the diverse disciplines of science together.
His Curse of dimensionality study integrates concerns from other disciplines, such as Theoretical computer science, Convolutional neural network and Special case.
The concepts of his Deep learning study are interwoven with issues in Artificial neural network, Function approximation, Gradient descent, Applied mathematics and Data set.
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