Pages that link to "Item:Q2054491"
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The following pages link to On the rate of convergence of fully connected deep neural network regression estimates (Q2054491):
Displaying 29 items.
- On the rate of convergence of image classifiers based on convolutional neural networks (Q2087403) (← links)
- Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local pooling (Q2112261) (← links)
- Convergence rates of deep ReLU networks for multiclass classification (Q2137813) (← links)
- Estimation of a regression function on a manifold by fully connected deep neural networks (Q2676904) (← links)
- On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data (Q2692553) (← links)
- Convergence rates for shallow neural networks learned by gradient descent (Q6137712) (← links)
- Deep nonparametric regression on approximate manifolds: nonasymptotic error bounds with polynomial prefactors (Q6172194) (← links)
- Optimal convergence rates of deep neural networks in a classification setting (Q6184926) (← links)
- Adaptive variational Bayes: optimality, computation and applications (Q6192331) (← links)
- Asset pricing with neural networks: significance tests (Q6193024) (← links)
- Analysis of the rate of convergence of two regression estimates defined by neural features which are easy to implement (Q6200889) (← links)
- Intrinsic and extrinsic deep learning on manifolds (Q6200905) (← links)
- A deep learning method for pricing high-dimensional American-style options via state-space partition (Q6543764) (← links)
- Calibrating multi-dimensional complex ODE from noisy data via deep neural networks (Q6556771) (← links)
- Local convergence rates of the nonparametric least squares estimator with applications to transfer learning (Q6565304) (← links)
- Deep learning based on randomized quasi-Monte Carlo method for solving linear Kolmogorov partial differential equation (Q6582041) (← links)
- Recovering the source term in elliptic equation via deep learning: method and convergence analysis (Q6586293) (← links)
- An error analysis for deep binary classification with sigmoid loss (Q6588360) (← links)
- Robust nonparametric regression based on deep ReLU neural networks (Q6592794) (← links)
- Convergence analysis for over-parameterized deep learning (Q6608346) (← links)
- Mini-workshop: Nonlinear approximation of high-dimensional functions in scientific computing. Abstracts from the mini-workshop held October 15--20, 2023 (Q6613392) (← links)
- Statistical theory for image classification using deep convolutional neural network with cross-entropy loss under the hierarchical max-pooling model (Q6616182) (← links)
- Layer sparsity in neural networks (Q6616187) (← links)
- How do noise tails impact on deep ReLU networks? (Q6621550) (← links)
- Adaptive deep learning for nonlinear time series models (Q6632604) (← links)
- Sampling complexity of deep approximation spaces (Q6649919) (← links)
- Factor Augmented Sparse Throughput Deep ReLU Neural Networks for High Dimensional Regression (Q6651371) (← links)
- Error analysis for empirical risk minimization over clipped ReLU networks in solving linear Kolmogorov partial differential equations (Q6662424) (← links)
- Applied harmonic analysis and data science. Abstracts from the workshop held April 21--26, 2024 (Q6671618) (← links)
