Pages that link to "Item:Q4317663"
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The following pages link to Neural Networks for Localized Approximation (Q4317663):
Displaying 40 items.
- Approximative compactness of linear combinations of characteristic functions (Q783713) (← links)
- Constructive approximate interpolation by neural networks (Q817488) (← links)
- Local antithetic sampling with scrambled nets (Q955143) (← links)
- Local approximation on artificial neural networks (Q1285315) (← links)
- On simultaneous approximations by radial basis function neural networks (Q1294160) (← links)
- Local feedback adjustment in neural networks with projection learning algorithm (Q1385778) (← links)
- Best approximation by linear combinations of characteristic functions of half-spaces. (Q1395802) (← links)
- Limitations of the approximation capabilities of neural networks with one hidden layer (Q1923890) (← links)
- Rates of approximation by neural network interpolation operators (Q2073064) (← links)
- DNN expression rate analysis of high-dimensional PDEs: application to option pricing (Q2117328) (← links)
- Nonlinear approximation and (deep) ReLU networks (Q2117331) (← links)
- Approximation spaces of deep neural networks (Q2117336) (← links)
- Limitations of shallow nets approximation (Q2292226) (← links)
- Application of radial basis function and generalized regression neural networks in nonlinear utility function specification for travel mode choice modelling (Q2476117) (← links)
- (Q3551822) (← links)
- Extension of localised approximation by neural networks (Q4243358) (← links)
- (Q4546521) (← links)
- Deep distributed convolutional neural networks: Universality (Q4560301) (← links)
- UNIFIED FRAMEWORK FOR MLPs AND RBFNs: INTRODUCING CONIC SECTION FUNCTION NETWORKS (Q4840224) (← links)
- Optimal Approximation with Sparsely Connected Deep Neural Networks (Q5025773) (← links)
- (Q5053184) (← links)
- Theoretical issues in deep networks (Q5073211) (← links)
- Full error analysis for the training of deep neural networks (Q5083408) (← links)
- Neural network interpolation operators activated by smooth ramp functions (Q5083442) (← links)
- Deep ReLU networks and high-order finite element methods (Q5132226) (← links)
- Better Approximations of High Dimensional Smooth Functions by Deep Neural Networks with Rectified Power Units (Q5162006) (← links)
- Deep neural networks for rotation-invariance approximation and learning (Q5236745) (← links)
- Approximating functions with multi-features by deep convolutional neural networks (Q5873927) (← links)
- Spline representation and redundancies of one-dimensional ReLU neural network models (Q5873929) (← links)
- A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations (Q5889064) (← links)
- A deep network construction that adapts to intrinsic dimensionality beyond the domain (Q6054952) (← links)
- Approximation capabilities of neural networks on unbounded domains (Q6055159) (← links)
- Approximating smooth and sparse functions by deep neural networks: optimal approximation rates and saturation (Q6062170) (← links)
- Neural network interpolation operators optimized by Lagrange polynomial (Q6077041) (← links)
- Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation (Q6107984) (← links)
- Learning sparse and smooth functions by deep sigmoid nets (Q6109261) (← links)
- Neural network interpolation operators of multivariate functions (Q6137791) (← links)
- Deep learning theory of distribution regression with CNNs (Q6168055) (← links)
- Approximation of functions from Korobov spaces by shallow neural networks (Q6544585) (← links)
- Learning and approximating piecewise smooth functions by deep sigmoid neural networks (Q6634146) (← links)
