Pages that link to "Item:Q2300759"

The following pages link to Universality of deep convolutional neural networks (Q2300759):

Displaying 50 items.

• Deep neural networks for rotation-invariance approximation and learning (Q5236745) (← links)
• Distributed Filtered Hyperinterpolation for Noisy Data on the Sphere (Q5855635) (← links)
• Learning rates for partially linear support vector machine in high dimensions (Q5856267) (← 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)
• Expressivity of Deep Neural Networks (Q5879776) (← links)
• Training a Neural-Network-Based Surrogate Model for Aerodynamic Optimisation Using a Gaussian Process (Q5880409) (← links)
• A deep network construction that adapts to intrinsic dimensionality beyond the domain (Q6054952) (← links)
• Theory of deep convolutional neural networks. III: Approximating radial functions (Q6055154) (← links)
• Neural network approximation of continuous functions in high dimensions with applications to inverse problems (Q6056231) (← links)
• Approximating smooth and sparse functions by deep neural networks: optimal approximation rates and saturation (Q6062170) (← links)
• Rates of approximation by ReLU shallow neural networks (Q6062171) (← links)
• Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
• Universality of gradient descent neural network training (Q6072577) (← links)
• DNN-based speech watermarking resistant to desynchronization attacks (Q6076555) (← links)
• Neural network interpolation operators optimized by Lagrange polynomial (Q6077041) (← links)
• Convergence of deep convolutional neural networks (Q6077046) (← links)
• Words as a window: using word embeddings to explore the learned representations of convolutional neural networks (Q6078692) (← links)
• Probabilistic robustness estimates for feed-forward neural networks (Q6079061) (← links)
• Arguments for the unsuitability of convolutional neural networks for non-local tasks (Q6079068) (← links)
• Approximation Analysis of Convolutional Neural Networks (Q6090346) (← links)
• Approximation error for neural network operators by an averaged modulus of smoothness (Q6093307) (← links)
• Universal regular conditional distributions via probabilistic transformers (Q6101232) (← links)
• Learning Optimal Multigrid Smoothers via Neural Networks (Q6108149) (← links)
• Scaling Up Bayesian Uncertainty Quantification for Inverse Problems Using Deep Neural Networks (Q6109143) (← links)
• Learning sparse and smooth functions by deep sigmoid nets (Q6109261) (← links)
• A continuous convolutional trainable filter for modelling unstructured data (Q6109268) (← links)
• Error analysis of kernel regularized pairwise learning with a strongly convex loss (Q6112862) (← links)
• SignReLU neural network and its approximation ability (Q6126040) (← links)
• Neural network interpolation operators of multivariate functions (Q6137791) (← links)
• Self-Supervised Deep Learning for Image Reconstruction: A Langevin Monte Carlo Approach (Q6144068) (← links)
• Connections between Operator-Splitting Methods and Deep Neural Networks with Applications in Image Segmentation (Q6151361) (← links)
• Error bounds for approximations using multichannel deep convolutional neural networks with downsampling (Q6155792) (← links)
• Using machine learning to model the training scalability of convolutional neural networks on clusters of GPUs (Q6161033) (← links)
• Deep learning theory of distribution regression with CNNs (Q6168055) (← links)
• Approximation of nonlinear functionals using deep ReLU networks (Q6170363) (← links)
• Learning ability of interpolating deep convolutional neural networks (Q6185680) (← links)
• Lu decomposition and Toeplitz decomposition of a neural network (Q6185692) (← links)
• Two-layer networks with the \(\text{ReLU}^k\) activation function: Barron spaces and derivative approximation (Q6191372) (← links)
• Approximation bounds for convolutional neural networks in operator learning (Q6403941) (← links)
• Approximation of smooth functionals using deep ReLU networks (Q6488836) (← links)
• Learning with centered reproducing kernels (Q6496339) (← links)
• Approximation analysis of CNNs from a feature extraction view (Q6496341) (← links)
• PottsMGNet: a mathematical explanation of encoder-decoder based neural networks (Q6541918) (← 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)
• Likelihood-Free Parameter Estimation with Neural Bayes Estimators (Q6585610) (← links)
• A unified Fourier slice method to derive ridgelet transform for a variety of depth-2 neural networks (Q6592797) (← links)
• Best approximation and inverse results for neural network operators (Q6595826) (← links)
• Recent developments in machine learning methods for stochastic control and games (Q6615618) (← links)