# Learning Deep Nearest Neighbor Representations Using Differentiable Boundary Trees

arXiv: https://arxiv.org/abs/1702.08833 · HTML: https://mlsanity.com/paper/1702.08833/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2017.

## Built on by (most cited first, 2 of 2)

- [Explaining the Success of Nearest Neighbor Methods in Prediction](https://mlsanity.com/paper/2502.15900/) (2025)
- [Neural Architecture for Online Ensemble Continual Learning](https://mlsanity.com/paper/2211.14963/) (2022)

## Abstract (arXiv)

Nearest neighbor (kNN) methods have been gaining popularity in recent years in light of advances in hardware and efficiency of algorithms. There is a plethora of methods to choose from today, each with their own advantages and disadvantages. One requirement shared between all kNN based methods is the need for a good representation and distance measure between samples. We introduce a new method called differentiable boundary tree which allows for learning deep kNN representations. We build on the recently proposed boundary tree algorithm which allows for efficient nearest neighbor classification, regression and retrieval. By modelling traversals in the tree as stochastic events, we are able to form a differentiable cost function which is associated with the tree's predictions. Using a deep neural network to transform the data and back-propagating through the tree allows us to learn good representations for kNN methods. We demonstrate that our method is able to learn suitable representations allowing for very efficient trees with a clearly interpretable structure.