| Kolmogorov's theorem and multilayer neural networks V Kůrková Neural networks 5 (3), 501-506, 1992 | 958 | 1992 |
| Kolmogorov's theorem is relevant V Kůrková Neural computation 3 (4), 617-622, 1991 | 269 | 1991 |
| Comparison of worst case errors in linear and neural network approximation V Kurková, M Sanguineti IEEE Transactions on Information Theory 48 (1), 264-275, 2002 | 155 | 2002 |
| Bounds on rates of variable-basis and neural-network approximation V Kurková, M Sanguineti IEEE Transactions on Information Theory 47 (6), 2659-2665, 2001 | 128 | 2001 |
| Estimates of the number of hidden units and variation with respect to half-spaces V Kůrková, PC Kainen, V Kreinovich Neural Networks 10 (6), 1061-1068, 1997 | 121 | 1997 |
| Dimension-independent rates of approximation by neural networks V Kůrková Computer Intensive Methods in Control and Signal Processing: The Curse of …, 1997 | 96 | 1997 |
| Functionally equivalent feedforward neural networks V Kůrková, PC Kainen Neural Computation 6 (3), 543-558, 1994 | 94 | 1994 |
| Error estimates for approximate optimization by the extended Ritz method V Kurková, M Sanguineti SIAM Journal on Optimization 15 (2), 461-487, 2005 | 86 | 2005 |
| International conference on artificial neural networks C Tan, F Sun, T Kong, W Zhang, C Yang, C Liu Springer, 2018 | 79 | 2018 |
| Representations and rates of approximation of real-valued Boolean functions by neural networks V Kůrková, P Savický, K Hlaváčková Neural Networks 11 (4), 651-659, 1998 | 79 | 1998 |
| Artificial neural networks and machine learning–ICANN 2018 C Tan, F Sun, T Kong, W Zhang, C Yang, C Liu, V Kůrková, ... Springer International Publishing, Cham, 270-279, 2018 | 74 | 2018 |
| Approximation of functions by perceptron networks with bounded number of hidden units V Kůrková Neural Networks 8 (5), 745-750, 1995 | 73 | 1995 |
| Learning with generalization capability by kernel methods of bounded complexity V Kůrková, M Sanguineti Journal of Complexity 21 (3), 350-367, 2005 | 68 | 2005 |
| Approximation by neural networks is not continuous PC Kainen, V Kůrková, A Vogt Neurocomputing 29 (1-3), 47-56, 1999 | 64 | 1999 |
| Complexity of Gaussian-radial-basis networks approximating smooth functions PC Kainen, V Kůrková, M Sanguineti Journal of Complexity 25 (1), 63-74, 2009 | 59 | 2009 |
| Recurrent neural networks: Some systems-theoretic aspects M Kárný, K Warwick, V Kůrková Dealing with complexity: A neural networks approach, 1-12, 1998 | 57 | 1998 |
| Geometric upper bounds on rates of variable-basis approximation V Kurková, M Sanguineti IEEE Transactions on Information Theory 54 (12), 5681-5688, 2008 | 47 | 2008 |
| An integral upper bound for neural network approximation PC Kainen, V Kůrková Neural Computation 21 (10), 2970-2989, 2009 | 46 | 2009 |
| Probabilistic lower bounds for approximation by shallow perceptron networks V Kůrková, M Sanguineti Neural Networks 91, 34-41, 2017 | 40 | 2017 |
| Complexity estimates based on integral transforms induced by computational units V Kůrková Neural Networks 33, 160-167, 2012 | 40 | 2012 |
Marcello SanguinetiDIBRIS - Universita' di GenovaVerified email at unige.it
