Pages that link to "Item:Q2399647"

The following pages link to Neural network with unbounded activation functions is universal approximator (Q2399647):

Displaying 45 items.

• Machine learning from a continuous viewpoint. I (Q829085) (← links)
• Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity (Q2021107) (← links)
• Topological properties of the set of functions generated by neural networks of fixed size (Q2031060) (← links)
• The universal approximation property. Characterization, construction, representation, and existence (Q2043428) (← links)
• Fast generalization error bound of deep learning without scale invariance of activation functions (Q2055056) (← links)
• Theory of deep convolutional neural networks. II: Spherical analysis (Q2057723) (← links)
• Symmetry \& critical points for a model shallow neural network (Q2077613) (← links)
• Understanding neural networks with reproducing kernel Banach spaces (Q2105111) (← links)
• Nonconvex regularization for sparse neural networks (Q2168678) (← links)
• On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces (Q2185697) (← links)
• Rapid estimation of permeability from digital rock using 3D convolutional neural network (Q2192827) (← links)
• A global universality of two-layer neural networks with ReLU activations (Q2236407) (← links)
• Misspecified diffusion models with high-frequency observations and an application to neural networks (Q2239259) (← links)
• Estimation of agent-based models using Bayesian deep learning approach of BayesFlow (Q2246641) (← links)
• A deep learning semiparametric regression for adjusting complex confounding structures (Q2247451) (← links)
• Deep learning as optimal control problems: models and numerical methods (Q2297872) (← links)
• A mean-field optimal control formulation of deep learning (Q2319864) (← links)
• Neural dynamic sliding mode control of nonlinear systems with both matched and mismatched uncertainties (Q2416716) (← links)
• The role of nonpolynomiality in uniform approximation by RBF networks of Hankel translates (Q2422470) (← links)
• ReLU Networks Are Universal Approximators via Piecewise Linear or Constant Functions (Q3386431) (← links)
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• Center Manifold Analysis of Plateau Phenomena Caused by Degeneration of Three-Layer Perceptron (Q5131192) (← links)
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• Regression methods in waveform modeling: a comparative study (Q5161932) (← links)
• On the double windowed ridgelet transform and its inverse (Q5210753) (← links)
• Piecewise linear functions representable with infinite width shallow ReLU neural networks (Q6052562) (← links)
• Theory of deep convolutional neural networks. III: Approximating radial functions (Q6055154) (← links)
• Approximation capabilities of neural networks on unbounded domains (Q6055159) (← links)
• Universal approximation properties for an ODENet and a ResNet: mathematical analysis and numerical experiments (Q6070659) (← links)
• A survey on modern trainable activation functions (Q6078705) (← links)
• Continuity properties of the shearlet transform and the shearlet synthesis operator on the Lizorkin type spaces (Q6093968) (← links)
• Deep reinforcement learning for adaptive mesh refinement (Q6095131) (← links)
• Explicit representations for Banach subspaces of Lizorkin distributions (Q6095743) (← links)
• Heaviside function as an activation function (Q6112992) (← links)
• Beating a Benchmark: Dynamic Programming May Not Be the Right Numerical Approach (Q6159077) (← links)
• An Interpretive Constrained Linear Model for ResNet and MgNet (Q6385646) (← links)
• Towards global neural network abstractions with locally-exact reconstruction (Q6488831) (← links)
• Distributional extension and invertibility of the \(k\)-plane transform and its dual (Q6571030) (← links)
• A unified Fourier slice method to derive ridgelet transform for a variety of depth-2 neural networks (Q6592797) (← links)
• The shearlet transform and asymptotic behavior of Lizorkin distributions (Q6608450) (← links)
• Learned query optimizers (Q6638534) (← links)
• From kernel methods to neural networks: a unifying variational formulation (Q6659493) (← links)