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Found 5 papers, 2 papers with code
Improving Uncertainty Calibration of Deep Neural Networks via Truth Discovery and Geometric Optimization
Deep Neural Networks (DNNs), despite their tremendous success in recent years, could still cast doubts on their predictions due to the intrinsic uncertainty associated with their learning process.
Meta-Learning with Neural Tangent Kernels
We achieve this goal by 1) replacing the adaptation with a fast-adaptive regularizer in the RKHS; and 2) solving the adaptation analytically based on the NTK theory.
Meta-Learning in Reproducing Kernel Hilbert Space
Within this paradigm, we introduce two meta learning algorithms in RKHS, which no longer need an explicit inner-loop adaptation as in the MAML framework.
Consistent $k$-Median: Simpler, Better and Robust
In this paper we introduce and study the online consistent $k$-clustering with outliers problem, generalizing the non-outlier version of the problem studied in [Lattanzi-Vassilvitskii, ICML17].
Graph Neural Networks with Composite Kernels
We then propose feature aggregation as the composition of the original neighbor-based kernel and a learnable kernel to encode feature similarities in a feature space.
