Lower bounds for approximation by MLP neural networks
From MaRDI portal
Publication:1305902
DOI10.1016/S0925-2312(98)00111-8zbMath0931.68093WikidataQ127580329 ScholiaQ127580329MaRDI QIDQ1305902
Publication date: 22 September 1999
Published in: Neurocomputing (Search for Journal in Brave)
Related Items
SelectNet: self-paced learning for high-dimensional partial differential equations, Linear and nonlinear approximation of spherical radial basis function networks, The estimate for approximation error of spherical neural networks, Approximation by ridge functions and neural networks with a bounded number of neurons, Geometric properties of the ridge function manifold, An interval uncertainty analysis method for structural response bounds using feedforward neural network differentiation, On the approximation by single hidden layer feedforward neural networks with fixed weights, Optimal approximation of piecewise smooth functions using deep ReLU neural networks, Neural network approximation: three hidden layers are enough, Approximation capabilities of neural networks on unbounded domains, Approximating smooth and sparse functions by deep neural networks: optimal approximation rates and saturation, Approximation by neural networks with a bounded number of nodes at each level, The Kolmogorov-Arnold representation theorem revisited, Phase transitions in rate distortion theory and deep learning, Lower bounds for artificial neural network approximations: a proof that shallow neural networks fail to overcome the curse of dimensionality, Approximation error of single hidden layer neural networks with fixed weights, Approximation by sums of ridge functions with fixed directions, A three layer neural network can represent any multivariate function, Essential rate for approximation by spherical neural networks, Computing the Approximation Error for Neural Networks with Weights Varying on Fixed Directions, A Single Hidden Layer Feedforward Network with Only One Neuron in the Hidden Layer Can Approximate Any Univariate Function, Approximation in shift-invariant spaces with deep ReLU neural networks, Negative results for approximation using single layer and multilayer feedforward neural networks, On the approximation by neural networks with bounded number of neurons in hidden layers, Approximation by neural networks with weights varying on a finite set of directions, Unnamed Item, Deep Network Approximation Characterized by Number of Neurons, Almost optimal estimates for approximation and learning by radial basis function networks, On lower bounds in radial basis approximation, Approximation by neural networks and learning theory, Unnamed Item, A neural network-based framework for financial model calibration, Pseudo-dimension and entropy of manifolds formed by affine-invariant dictionary, Some problems in the theory of ridge functions, Limitations of shallow nets approximation, The capability of approximation for neural networks interpolant on the sphere, Complexity of Shallow Networks Representing Finite Mappings, Model reduction and neural networks for parametric PDEs, Equivalence of approximation by convolutional neural networks and fully-connected networks, Approximation rates for neural networks with encodable weights in smoothness spaces, On the representation by bivariate ridge functions, Deep neural networks for rotation-invariance approximation and learning, Deep Network With Approximation Error Being Reciprocal of Width to Power of Square Root of Depth, Optimal Approximation with Sparsely Connected Deep Neural Networks, On best approximation of classes by radial functions, A theoretical analysis of deep neural networks and parametric PDEs, Approximation spaces of deep neural networks
