Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning

By Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel. 2013

35 citations recorded by the source corpus.

Subfields: Optimization & Training

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Optimization & Training

1. Amaury Habrard
2. Aurélien Bellet
3. Marc Sebban
4. Pong Chi Yuen
5. Xu Zhang
6. Mang Ye
7. Shih-Fu Chang

Citations in the corpus

• Unsupervised Embedding Learning via Invariant and Spreading Instance Feature (2019, methodology): “2) Estimating Between-image Labels, it usually estimates between-image labels using the clustering technique [3, 9, 26] or kNN-based methods [41], which provide label information.”
• A Survey on Metric Learning for Feature Vectors and Structured Data (2013, background): “Hong et al. (2011) later proposed to learn a mixture of NCA metrics, while Tarlow et al. (2013) generalize NCA to k-NN with k > 1.”