Meta-Learning Algorithms
Kernel Inducing Points
Introduced by Nguyen et al. in Dataset Meta-Learning from Kernel Ridge-RegressionKernel Inducing Points, or KIP, is a meta-learning algorithm for learning datasets that can mitigate the challenges which occur for naturally occurring datasets without a significant sacrifice in performance. KIP uses kernel-ridge regression to learn ?$\epsilon$-approximate datasets. It can be regarded as an adaption of the inducing point method for Gaussian processes to the case of Kernel Ridge Regression.
Source: Dataset Meta-Learning from Kernel Ridge-Regression
Papers
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Usage Over Time
Components
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| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
