Nearest Neighbor Methods
Rethinking Kernel Methods for Node Representation Learning on Graphs
Yu Tian, Long Zhao, Xi Peng, Dimitris Metaxas
Thegraphkernelfunction can be utilized toclassify graphs via standard kernel methods such assupport vector machines or k-nearest neighbors.
2D-OOB: Attributing Data Contribution Through Joint Valuation Framework
Data valuation has emerged as a powerful framework for quantifying each datum's
Embedding Dimension of Contrastive Learning and k-Nearest Neighbors
We study the embedding dimension of distance comparison data in two settings: contrastive learning and k -nearest neighbors ( k -NN).
No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations
The resulting features are evaluated on k-nearest neighbor classification over 11 datasets from vision, 5 from natural language processing, and 2 from audio.