12 citations · 13 across the 4 of their papers we have counts for
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cs.LG2023★ 1 cited
An Exact Kernel Equivalence for Finite Classification Models
Brian Bell, Michael Geyer, David Glickenstein +2
We explore the equivalence between neural networks and kernel methods by deriving the first exact representation of any finite-size parametric classification model trained with gra…
cs.CV2023
Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification
Kilian Zepf, Selma Wanna, Marco Miani +5
Image segmentation relies heavily on neural networks which are known to be overconfident, especially when making predictions on out-of-distribution (OOD) images. This is a common s…