Randomized Partition Trees for Nearest Neighbor Search

By Sanjoy Dasgupta, Kaushik Sinha. 2015

64 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Information Retrieval & Search, Algorithms & Complexity, Signal Processing

How researchers see this paper

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

In Computer Graphics and Computer-Aided Design

1. J. Langford
2. Alina Beygelzimer

In Computer Vision and Pattern Recognition

1. J. Langford
2. Hal Daumé Iii
3. Trevor Darrell
4. Wen Sun
5. Alina Beygelzimer
6. Paul Mineiro
7. Luc Van Gool
8. Jitendra Malik

In Information Retrieval & Search

1. J. Langford
2. Wen Sun
3. Hal Daumé Iii
4. Alina Beygelzimer
5. Paul Mineiro

In Algorithms & Complexity

1. J. Langford
2. Hal Daumé Iii
3. Thorsten Joachims
4. Alekh Agarwal
5. Adith Swaminathan
6. Francesco Orabona
7. Christopher D. Rosin

In Signal Processing

1. Wen Sun
2. J. Langford
3. Hal Daumé Iii
4. Alina Beygelzimer
5. L. V. D. van der Maaten
6. Parikshit Ram
7. Alexander G. Gray
8. Peter D. Grünwald

Citations in the corpus

• Contextual Memory Trees (2018, methodology): “…nearest neighbor algorithms (including memory systems that use them (Kaiser et al., 2017)) are computationally inefficient except in special cases (Dasgupta & Sinha, 2015; Beygelzimer et al., 2006) and do not learn. b) Approximate Nearest Neigh-bors via Locality-Sensitive Hashing (Datar et al.,…”