Pages that link to "Item:Q2117338"
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The following pages link to Universal approximations of invariant maps by neural networks (Q2117338):
Displaying 17 items.
- Equivariant deep learning via morphological and linear scale space PDEs on the space of positions and orientations (Q826129) (← links)
- Ehresmann connections and feedforward neural networks (Q1596936) (← links)
- \(\mathrm{SU}(1,1)\) equivariant neural networks and application to robust Toeplitz Hermitian positive definite matrix classification (Q2117902) (← links)
- Iterative SE(3)-transformers (Q2117906) (← links)
- Universal approximation of symmetric and anti-symmetric functions (Q2149203) (← links)
- Homogeneous vector bundles and \(G\)-equivariant convolutional neural networks (Q2164653) (← links)
- The universal approximation theorem for complex-valued neural networks (Q2689134) (← links)
- (Q3445670) (← links)
- (Q4246292) (← links)
- Full error analysis for the training of deep neural networks (Q5083408) (← links)
- A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations (Q5889064) (← links)
- Neural network approximation of continuous functions in high dimensions with applications to inverse problems (Q6056231) (← links)
- What is... an Equivariant Neural Network? (Q6073079) (← links)
- TransNet: shift invariant transformer network for side channel analysis (Q6103091) (← links)
- Piecewise integrable neural network: an interpretable chaos identification framework (Q6572660) (← links)
- A unified Fourier slice method to derive ridgelet transform for a variety of depth-2 neural networks (Q6592797) (← links)
- Statistical theory for image classification using deep convolutional neural network with cross-entropy loss under the hierarchical max-pooling model (Q6616182) (← links)
