The following pages link to (Q5053261):
Displaying 14 items.
- Make \(\ell_1\) regularization effective in training sparse CNN (Q782914) (← links)
- Approximation of functions from korobov spaces by deep convolutional neural networks (Q2108977) (← links)
- Neural network training using \(\ell_1\)-regularization and bi-fidelity data (Q2138992) (← links)
- On the landscape of one-hidden-layer sparse networks and beyond (Q2152502) (← links)
- Inference, learning and attention mechanisms that exploit and preserve sparsity in CNNs (Q2193592) (← links)
- SVD-based DNN pruning and retraining (Q2987191) (← links)
- Deep Learning as Sparsity-Enforcing Algorithms (Q5879781) (← links)
- Sparse Deep Neural Network for Nonlinear Partial Differential Equations (Q5885722) (← links)
- A brain-inspired algorithm for training highly sparse neural networks (Q6097119) (← links)
- Sparse learning model with embedded RIP conditions for turbulence super-resolution reconstruction (Q6497206) (← links)
- Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics (Q6550128) (← links)
- Exploring the impact of post-training rounding in regression models. (Q6584366) (← links)
- Enhancing deep learning algorithm accuracy and stability using multicriteria optimization: an application to distributed learning with MNIST digits (Q6589086) (← links)
- Group projected subspace pursuit for block sparse signal reconstruction: convergence analysis and applications (Q6657433) (← links)
