Pages that link to "Item:Q2117331"

The following pages link to Nonlinear approximation and (deep) ReLU networks (Q2117331):

Displaying 50 items.

• Linearized two-layers neural networks in high dimension (Q2039801) (← links)
• High-dimensional distribution generation through deep neural networks (Q2062235) (← links)
• Constructive deep ReLU neural network approximation (Q2067309) (← links)
• The construction and approximation of ReLU neural network operators (Q2086452) (← links)
• Why rectified linear activation functions? Why max-pooling? A possible explanation (Q2101281) (← links)
• Stable recovery of entangled weights: towards robust identification of deep neural networks from minimal samples (Q2105108) (← links)
• Information theory and recovery algorithms for data fusion in Earth observation (Q2106495) (← links)
• Depth separations in neural networks: what is actually being separated? (Q2117335) (← links)
• Approximation spaces of deep neural networks (Q2117336) (← links)
• Exponential ReLU DNN expression of holomorphic maps in high dimension (Q2117341) (← links)
• Adaptive two-layer ReLU neural network. I: Best least-squares approximation (Q2122629) (← links)
• Machine learning design of volume of fluid schemes for compressible flows (Q2123342) (← links)
• Thermodynamically consistent physics-informed neural networks for hyperbolic systems (Q2136443) (← links)
• A mesh-free method using piecewise deep neural network for elliptic interface problems (Q2141617) (← links)
• Approximation properties of deep ReLU CNNs (Q2157922) (← links)
• ReLU deep neural networks from the hierarchical basis perspective (Q2159911) (← links)
• On the SQH method for solving optimal control problems with non-smooth state cost functionals or constraints (Q2161052) (← links)
• Designing rotationally invariant neural networks from PDEs and variational methods (Q2168880) (← links)
• Nonlinear approximation via compositions (Q2185653) (← links)
• Universal approximation with quadratic deep networks (Q2185719) (← links)
• A functional equation with polynomial solutions and application to neural networks (Q2219991) (← links)
• Error bounds for approximations with deep ReLU networks (Q2292227) (← links)
• Neural network with unbounded activation functions is universal approximator (Q2399647) (← links)
• Optimal stable nonlinear approximation (Q2671290) (← links)
• Best \(n\)-term approximation of diagonal operators and application to function spaces with mixed smoothness (Q2678414) (← links)
• Deep vs. shallow networks: an approximation theory perspective (Q2835988) (← links)
• ReLU Networks Are Universal Approximators via Piecewise Linear or Constant Functions (Q3386431) (← links)
• Approximation by Combinations of ReLU and Squared ReLU Ridge Functions With <inline-formula> <tex-math notation="LaTeX">$\ell^1$ </tex-math> </inline-formula> and <inline-formula> (Q4562132) (← links)
• Deep Neural Network Approximation Theory (Q5001568) (← links)
• (Q5053289) (← links)
• A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations (Q5058646) (← links)
• Theoretical issues in deep networks (Q5073211) (← links)
• Deep ReLU Networks Overcome the Curse of Dimensionality for Generalized Bandlimited Functions (Q5079533) (← links)
• Neural Parametric Fokker--Planck Equation (Q5087103) (← links)
• Deep Neural Network Surrogates for Nonsmooth Quantities of Interest in Shape Uncertainty Quantification (Q5097855) (← links)
• A note on the applications of one primary function in deep neural networks (Q5097859) (← links)
• Deep learning-based approximation of Goldbach partition function (Q5101877) (← links)
• (Q5104591) (← links)
• A note on the expressive power of deep rectified linear unit networks in high‐dimensional spaces (Q5223573) (← links)
• Spline representation and redundancies of one-dimensional ReLU neural network models (Q5873929) (← links)
• Expressivity of Deep Neural Networks (Q5879776) (← links)
• Sparse Deep Neural Network for Nonlinear Partial Differential Equations (Q5885722) (← links)
• Neural network approximation (Q5887830) (← links)
• Simultaneous neural network approximation for smooth functions (Q6052416) (← links)
• Approximation capabilities of neural networks on unbounded domains (Q6055159) (← links)
• Deep ReLU neural network approximation in Bochner spaces and applications to parametric PDEs (Q6062166) (← links)
• Universality of gradient descent neural network training (Q6072577) (← links)
• A convergent deep learning algorithm for approximation of polynomials (Q6073137) (← links)
• Convergence of deep convolutional neural networks (Q6077046) (← links)
• Mesh-informed neural networks for operator learning in finite element spaces (Q6077303) (← links)