Pages that link to "Item:Q2077601"
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The following pages link to Kernel methods for center manifold approximation and a weak data-based version of the center manifold theorem (Q2077601):
Displaying 10 items.
- One-shot learning of stochastic differential equations with data adapted kernels (Q2111726) (← links)
- Learning dynamical systems from data: a simple cross-validation perspective. III: Irregularly-sampled time series (Q2677775) (← links)
- A note on microlocal kernel design for some slow-fast stochastic differential equations with critical transitions and application to EEG signals (Q2700697) (← links)
- (Q2765728) (← links)
- Learning dynamical systems from data: a simple cross-validation perspective. IV: Case with partial observations (Q6096532) (← links)
- Kernel methods for center manifold approximation and a data-based version of the Center Manifold Theorem (Q6354987) (← links)
- Learning dynamical systems from data: a simple cross-validation perspective. V: Sparse kernel flows for 132 chaotic dynamical systems (Q6496480) (← links)
- Simplicity bias, algorithmic probability, and the random logistic map (Q6554925) (← links)
- Bridging algorithmic information theory and machine learning: a new approach to kernel learning (Q6558847) (← links)
- Hausdorff metric based training of kernels to learn attractors with application to 133 chaotic dynamical systems (Q6558876) (← links)
