Pages that link to "Item:Q5873927"
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The following pages link to Approximating functions with multi-features by deep convolutional neural networks (Q5873927):
Displaying 17 items.
- Function approximation by deep neural networks with parameters \(\{0, \pm \frac{1}{2}, \pm 1,2\}\) (Q2074650) (← links)
- Function approximation by deep networks (Q2191837) (← links)
- Maximal Function Pooling with Applications (Q5020148) (← links)
- Some new inequalities and numerical results of bivariate Bernstein-type operator including Bézier basis and its GBS operator (Q6112850) (← links)
- Approximation of nonlinear functionals using deep ReLU networks (Q6170363) (← links)
- Learning with centered reproducing kernels (Q6496339) (← links)
- Approximation analysis of CNNs from a feature extraction view (Q6496341) (← links)
- On the convergence of gradient descent for robust functional linear regression (Q6564669) (← links)
- Distributed robust regression with correntropy losses and regularization kernel networks (Q6564882) (← links)
- Approximation by frame-like multiwavelets (Q6580206) (← links)
- Relevant sampling in a reproducing kernel subspace of Orlicz space (Q6587595) (← links)
- Approximating reproducing kernel Hilbert space functions by Bernstein operators (Q6617623) (← links)
- Approximation properties of Riemann-Liouville type fractional Bernstein-Kantorovich operators of order \(\alpha\) (Q6633220) (← links)
- Smoothing piecewise linear activation functions based on mollified square root functions (Q6633224) (← links)
- A generalization of Szász operators with the help of new kind Appell polynomials (Q6634145) (← links)
- Learning and approximating piecewise smooth functions by deep sigmoid neural networks (Q6634146) (← links)
- Some new fractional integral inequalities for \((h_1, h_2)\)-convex functions (Q6634147) (← links)
