Pages that link to "Item:Q2297872"
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The following pages link to Deep learning as optimal control problems: models and numerical methods (Q2297872):
Displaying 35 items.
- Machine learning from a continuous viewpoint. I (Q829085) (← links)
- Deep neural networks and mixed integer linear optimization (Q1617390) (← links)
- A projected primal-dual gradient optimal control method for deep reinforcement learning (Q1980960) (← links)
- Variational networks: an optimal control approach to early stopping variational methods for image restoration (Q1988355) (← links)
- Classification with Runge-Kutta networks and feature space augmentation (Q2063032) (← links)
- A measure theoretical approach to the mean-field maximum principle for training NeurODEs (Q2105521) (← links)
- Deep learning for inverse problems. Abstracts from the workshop held March 7--13, 2021 (hybrid meeting) (Q2131206) (← links)
- Preface. Special issue in honor of Reinout Quispel (Q2297879) (← links)
- Deep relaxation: partial differential equations for optimizing deep neural networks (Q2319762) (← links)
- A mean-field optimal control formulation of deep learning (Q2319864) (← links)
- Linear-quadratic stochastic delayed control and deep learning resolution (Q2664898) (← links)
- A framework for randomized time-splitting in linear-quadratic optimal control (Q2671271) (← links)
- Control on the manifolds of mappings with a view to the deep learning (Q2676673) (← links)
- Neural control of discrete weak formulations: Galerkin, least squares \& minimal-residual methods with quasi-optimal weights (Q2679332) (← links)
- Optimal control by deep learning techniques and its applications on epidemic models (Q2684035) (← links)
- An approach to solving optimal control problems of nonlinear systems by introducing detail-reward mechanism in deep reinforcement learning (Q2688600) (← links)
- (Q4998938) (← links)
- Structure-preserving deep learning (Q5014474) (← links)
- Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (Q5019943) (← links)
- Deep learning for ranking response surfaces with applications to optimal stopping problems (Q5139253) (← links)
- Machine Learning: Deepest Learning as Statistical Data Assimilation Problems (Q5157217) (← links)
- Neural Approximations for Optimal Control and Decision (Q5243078) (← links)
- Turnpike in optimal control of PDEs, ResNets, and beyond (Q5887835) (← links)
- Deep neural networks on diffeomorphism groups for optimal shape reparametrization (Q6093564) (← links)
- Deep learning approximation of diffeomorphisms via linear-control systems (Q6099195) (← links)
- Sparsity in long-time control of neural ODEs (Q6099693) (← links)
- Data-driven robust optimization using deep neural networks (Q6109292) (← links)
- Neural ODE Control for Classification, Approximation, and Transport (Q6115450) (← links)
- Geometric methods for adjoint systems (Q6188984) (← links)
- Optimal Dirichlet boundary control by Fourier neural operators applied to nonlinear optics (Q6196628) (← links)
- PottsMGNet: a mathematical explanation of encoder-decoder based neural networks (Q6541918) (← links)
- An optimal control framework for adaptive neural ODEs (Q6561374) (← links)
- On properties of adjoint systems for evolutionary PDEs (Q6616420) (← links)
- Double-well net for image segmentation (Q6669799) (← links)
- Constrained dynamics, stochastic numerical methods and the modeling of complex systems. Abstracts from the workshop held May 26--31, 2024 (Q6671623) (← links)
