Věra Kůrková | Academic Influence

Věra Kůrková's AcademicInfluence.com Rankings

Věra Kůrková

Computer Science

#2806

World Rank

#2937

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Artificial Intelligence

#5080

World Rank

#5146

Historical Rank

computer-science Degrees

Věra Kůrková

Mathematics

#5590

World Rank

#7856

Historical Rank

Measure Theory

#3114

World Rank

#3695

Historical Rank

mathematics Degrees

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Computer Science
Mathematics

Věra Kůrková's Degrees

PhD Mathematics Charles University

Why Is Věra Kůrková Influential?

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According to Wikipedia, Věra Kůrková is a Czech mathematician and computer scientist, affiliated with the Institute of Computer Science of the Czech Academy of Sciences. Her research interests include neural networks, computational learning theory, and nonlinear approximation theory. She formulated the abstract concept of a variational norm in 1997 which puts ideas of Maurey, Jones, and Barron into the context of functional analysis. See V. Kůrková, Dimension-independent rates of approximation by neural networks. In: Warwick, K., Karny, M. Computer-Intensive Methods in Control and Signal Processing. The Curse of Dimensionality, Birkhauser, Boston, MA, pp. 261–270 . See also F. Girosi and G. Anzellotti, Convergence rates of approximation by translates, MIT Artificial Intelligence Laboratory, AI Memo No. 1288, April 1995, C.B.I.P. Paper No. 73. Kůrková is also known for the concept of quasiorthogonal set which she developed jointly with Robert Hecht-Nielsen and Paul Kainen.

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Věra Kůrková's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Kolmogorov's theorem and multilayer neural networks (1992) (675)
Comparison of worst case errors in linear and neural network approximation (2002) (154)
Kolmogorov's Theorem Is Relevant (1991) (135)
Estimates of the Number of Hidden Units and Variation with Respect to Half-Spaces (1997) (110)
Bounds on rates of variable-basis and neural-network approximation (2001) (105)
Artificial Neural Networks - ICANN 2008 , 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part I (2008) (99)
Dimension-Independent Rates of Approximation by Neural Networks (1997) (97)
Dealing with Complexity (1998) (83)
Rates of approximation of real-valued boolean functions by neural networks (1998) (75)
Functionally Equivalent Feedforward Neural Networks (1994) (69)
Error Estimates for Approximate Optimization by the Extended Ritz Method (2005) (68)
Complexity of Gaussian-radial-basis networks approximating smooth functions (2009) (61)
Approximation of functions by perceptron networks with bounded number of hidden units (1995) (60)
Learning with generalization capability by kernel methods of bounded complexity (2005) (60)
An Integral Upper Bound for Neural Network Approximation (2009) (49)
Quasiorthogonal dimension of euclidean spaces (1993) (48)
Dependence of Computational Models on Input Dimension: Tractability of Approximation and Optimization Tasks (2012) (47)
Approximation by neural networks is not continuous (1999) (47)
Geometric Upper Bounds on Rates of Variable-Basis Approximation (2008) (45)
A Sobolev-type upper bound for rates of approximation by linear combinations of Heaviside plane waves (2007) (33)
Minimization of Error Functionals over Variable-Basis Functions (2003) (30)
Approximate Minimization of the Regularized Expected Error over Kernel Models (2008) (28)
Probabilistic lower bounds for approximation by shallow perceptron networks (2017) (28)
Best approximation by Heaviside perceptron networks (2000) (25)
Some comparisons of complexity in dictionary-based and linear computational models (2011) (25)
Model complexities of shallow networks representing highly varying functions (2016) (24)
Artificial Neural Networks and Machine Learning – ICANN 2018 (2018) (21)
A Brain-Like Design to Learn Optimal Decision Strategies in Complex Environments (1998) (21)
Minimization of Error Functionals over Perceptron Networks (2008) (21)
Best approximation by linear combinations of characteristic functions of half-spaces (2003) (20)
Continuity of Approximation by Neural Networks in Lp Spaces (2001) (19)
Complexity estimates based on integral transforms induced by computational units (2012) (19)
Comparing fixed and variable-width Gaussian networks (2014) (19)
Approximating Multivariable Functions by Feedforward Neural Nets (2013) (17)
Neural Network Learning as an Inverse Problem (2005) (17)
Incremental Approximation by Neural Networks (1998) (16)
Estimates of covering numbers of convex sets with slowly decaying orthogonal subsets (2007) (16)
Can dictionary-based computational models outperform the best linear ones? (2011) (16)
Kolmogorov's theorem (1998) (16)
Integral combinations of Heavisides (2010) (15)
Constructive lower bounds on model complexity of shallow perceptron networks (2018) (13)
Can Two Hidden Layers Make a Difference? (2013) (13)
Lower Bounds on Complexity of Shallow Perceptron Networks (2016) (13)
Universal Approximation Using Feedforward Neural Networks with Gaussian Bar Units (1992) (12)
Uniqueness of Functional Representations by Gaussian Basis Function Networks (1994) (12)
Classification by Sparse Neural Networks (2019) (11)
Quasiorthogonal Dimension (2020) (11)
Estimates of Approximation Rates by Gaussian Radial-Basis Functions (2007) (11)
Model Complexity of Neural Networks and Integral Transforms (2009) (10)
Accuracy of approximations of solutions to Fredholm equations by kernel methods (2012) (10)
Dealing with complexity : a neural networks approach (1998) (10)
Uniqueness of network parametrization and faster learning (1994) (9)
Singularities of finite scaling functions (1996) (9)
Approximation of Functions by Neural Networks (1998) (8)
Some Comparisons of Networks with Radial and Kernel Units (2012) (7)
Learning from Data as an Optimization and Inverse Problem (2010) (7)
Some insights from high-dimensional spheres: Comment on "The unreasonable effectiveness of small neural ensembles in high-dimensional brain" by Alexander N. Gorban et al. (2019) (7)
Artificial Neural Nets and Genetic Algorithms (2001) (6)
Surrogate Modelling of Solutions of Integral Equations by Neural Networks (2012) (6)
Universality and Complexity of Approximation of Multivariable Functions by Feedforward Networks (2002) (5)
Probabilistic Bounds for Binary Classification of Large Data Sets (2019) (5)
Recurrent Neural Networks: Some Systems-Theoretic Aspects (1998) (5)
On Tractability of Neural-Network Approximation (2009) (5)
Feature Selection and Classification by a Modified Model with Latent Structure (1998) (5)
Rates of Approximation of Multivariable Functions by One-hidden-layer Neural Networks (1998) (4)
Bounds for Approximate Solutions of Fredholm Integral Equations Using Kernel Networks (2011) (4)
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I (2019) (4)
Approximative compactness of linear combinations of characteristic functions (2020) (4)
Tightness of Upper Bounds on Rates of Neural-Network Approximation (2001) (4)
Kernel Networks with Fixed and Variable Widths (2011) (3)
Brain-inspired computing and machine learning (2020) (3)
On the Effectiveness of Memory-Based Methods in Machine Learning (1998) (3)
A Priori Information in Network Design (1998) (3)
Some Comparisons of Model Complexity in Linear and Neural-Network Approximation (2010) (3)
Rates of Minimization of Error Functionals over Boolean Variable-Basis Functions (2005) (3)
A geometric method to obtain error-correcting classification by neural networks with fewer hidden units (1996) (3)
Tight Bounds on Rates of Neural-Network Approximation (2001) (3)
Complexity of Shallow Networks Representing Finite Mappings (2015) (3)
Upper Bounds on the Approximation Rates of Real-valued Boolean Functions by Neural Networks (1997) (2)
Surrogate solutions of Fredholm equations by feedforward networks (2012) (2)
Approximation of continuous functions by RBF and KBF networks (1994) (2)
The Use of State Space Control Theory for Analysing Feedforward Neural Networks (1998) (2)
From theoretical issues and neurophysiology to applications and hardware implementations. (2010) (2)
Geometric Rates of Approximation by Neural Networks (2008) (2)
Complexity of Shallow Networks Representing Functions with Large Variations (2014) (2)
Probabilistic Bounds on Complexity of Networks Computing Binary Classification Tasks (2018) (2)
Comparison of rates of linear and neural network approximation (2000) (2)
Inverse Problems in Learning from Data (2016) (2)
Estimates of Network Complexity and Integral Representations (2008) (2)
Artificial neural nets and genetic algorithms : proceedings of the International Conference in Prague, Czech Republic, 2001 (2001) (2)
Approximation of Smooth Functions by Neural Networks (1998) (2)
Correlations of random classifiers on large data sets (2021) (1)
Model complexities of shallow neural networks for the approximation of input-output mappings with large variations (2015) (1)
Some comparisons of the worst-case errors in linear and neural network approximation (2000) (1)
Supervised Learning as an Inverse Problem (2020) (1)
Artificial Neural Networks - ICANN 2008, 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part II (2008) (1)
Sparsity and Complexity of Networks Computing Highly-Varying Functions (2018) (1)
Limitations of Shallow Networks (2019) (1)
Artificial Neural Networks and Machine Learning – ICANN 2018 (2018) (1)
Statistical Decision Making and Neural Networks (1998) (1)
Differential Neurocontrol of Multidimensional Systems (1998) (1)
Minimization of empirical error over perceptron networks (2005) (1)
Recent Results and Mathematical Methods for Functional Approximation by Neural Networks (1998) (1)
Learning from data by neural networks with a limited complexity � (1)
Limitations of One-Hidden-Layer Perceptron Networks (2015) (1)
Limitations of shallow networks representing finite mappings (2018) (1)
Sparsity of Shallow Networks Representing Finite Mappings (2017) (1)
Representations of Highly-Varying Functions by One-Hidden-Layer Networks (2014) (1)
Neural network learning as approximate optimization (2003) (1)
Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II (2019) (0)
Probabilistic Bounds for Approximation by Neural Networks (2019) (0)
Tight Bounds on Rates of Variable-Basis Approximation via Estimates of Covering Numbers (2020) (0)
Brain-inspired computing and machine learning (2020) (0)
Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008) (0)
A Study of Non Mean Square Error Criteria for the Training of Neural Networks (1998) (0)
Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part III (2019) (0)
Approximation by Perceptron Networks (2001) (0)
Neurofuzzy Systems Modelling: A Transparent Approach (1998) (0)
Multivariable Approximation by Convolutional Kernel Networks (2016) (0)
Book and Media Review Editor (2008) (0)
5 Estimates of Model Complexity in Neural-Network Learning (2009) (0)
Model Complexity of Neural Networks in High-Dimensional Approximation (2012) (0)
Estimates of Model Complexity in Neural-Network Learning (2009) (0)
Translation-Invariant Kernels for Multivariable Approximation (2020) (0)
Integral Transforms Induced by Heaviside Perceptrons (2020) (0)
Tight bounds on rates of variable-basis approximation 1 (2006) (0)
Limitations of shallow networks representing finite mappings (2018) (0)
Learning as an Inverse Problem in Reproducing Kernel Hilbert (2020) (0)
Approximation of Classifiers by Deep Perceptron Networks (2023) (0)
Generalization in Learning from Examples (2007) (0)
ITAT 2014: Information Technologies – Applications and Theory, Part II: Proceedings of the 14th conference ITAT 2014 – Workshops and Posters (2014) (0)
Approximation of functions by Gaussian RBF networks with bouded number of hidden units (1995) (0)
Inverse problems in data analysis (2006) (0)
The Psychological Limits of Neural Computation (1998) (0)
Discrete Event Complex Systems: Scheduling with Neural Networks (1998) (0)
Rates of Approximation in a Feedforward Network Depend on the Type of Computational Unit (1998) (0)
Accuracy of Surrogate Solutions of Integral Equations by Feedforward Networks (2014) (0)
Bounds on Sparsity of One-Hidden-Layer Perceptron Networks (2017) (0)
Estimates of Data Complexity in Neural-Network Learning (2007) (0)
the Czech Republic Minimization of error functionals over variable-basis functions 1 (2017) (0)
Guest editorial: Adaptive and natural computing algorithms (2012) (0)
Cardinal functions on modifications of uniform spaces and fine uniform spaces (1982) (0)
A Sobolev-type upper bound for rates of approximation by linear combinations of plane waves (2005) (0)
Constructive lower bounds on model complexity of shallow perceptron networks (2017) (0)
MAPPINGS BETWEEN HIGH-DIMENSIONAL REPRESENTATIONS IN CONNECTIONISTIC SYSTEMS (2004) (0)
Chapter 5 Approximating Multivariable Functions by Feedforward Neural Nets (2013) (0)
Geometric Algebra Based Neural Networks (1998) (0)
A Tutorial on the EM Algorithm and Its Applications to Neural Network Learning (1998) (0)
Rates of Approximation of Smooth Functions by Gaussian Radial-Basis Networks (2020) (0)

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What Schools Are Affiliated With Věra Kůrková?

Věra Kůrková is affiliated with the following schools:

Charles University