Pages that link to "Item:Q5381118"
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The following pages link to A representer theorem for deep kernel learning (Q5381118):
Displaying 13 items.
- Do ideas have shape? Idea registration as the continuous limit of artificial neural networks (Q2111734) (← links)
- Learning rates for the kernel regularized regression with a differentiable strongly convex loss (Q2191832) (← links)
- A unifying representer theorem for inverse problems and machine learning (Q2231644) (← links)
- (Q5053232) (← links)
- What Kinds of Functions Do Deep Neural Networks Learn? Insights from Variational Spline Theory (Q5071660) (← links)
- (Q5214198) (← links)
- Statistical inference using regularized M-estimation in the reproducing kernel Hilbert space for handling missing data (Q6058524) (← links)
- Data-Driven Kernel Designs for Optimized Greedy Schemes: A Machine Learning Perspective (Q6154961) (← links)
- Kernel-based linear system identification: when does the representer theorem hold? (Q6537302) (← links)
- Multiresolution kernel matrix algebra (Q6562913) (← links)
- Reproducing property of bounded linear operators and kernel regularized least square regressions (Q6591721) (← links)
- Deep networks for system identification: a survey (Q6659190) (← links)
- Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems (Q6667327) (← links)
