Pages that link to "Item:Q6172665"
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The following pages link to Deep learning via dynamical systems: an approximation perspective (Q6172665):
Displaying 16 items.
- DANTE: deep alternations for training neural networks (Q2057720) (← links)
- Learning dynamical systems from data: a simple cross-validation perspective. I: Parametric kernel flows (Q2077645) (← links)
- Computing the invariant distribution of randomly perturbed dynamical systems using deep learning (Q2149015) (← links)
- A proposal on machine learning via dynamical systems (Q2401491) (← links)
- Riddled basins of the optimal states in learning dynamical systems (Q2563832) (← links)
- (Q4010437) (← links)
- Deep Network Approximation for Smooth Functions (Q5155613) (← links)
- Deep neural networks can stably solve high-dimensional, noisy, non-linear inverse problems (Q5873926) (← links)
- Neural ODE Control for Classification, Approximation, and Transport (Q6115450) (← links)
- Dynamical Systems–Based Neural Networks (Q6181900) (← links)
- On mathematical modeling in image reconstruction and beyond (Q6200218) (← links)
- On dynamical system modeling of learned primal-dual with a linear operator \(\mathcal{K}\): stability and convergence properties (Q6557695) (← links)
- An optimal control framework for adaptive neural ODEs (Q6561374) (← links)
- A hybrid Sobolev gradient method for learning NODEs (Q6620788) (← links)
- Vanilla feedforward neural networks as a discretization of dynamical systems (Q6645925) (← links)
- Training neural networks from an ergodic perspective (Q6655492) (← links)
