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Semantic Scholar uses AI to extract papers important to this topic.

Review

2018

Review

2018

Nearest neighbor search is a fundamental problem in various domains, such as computer vision, data mining, and machine learning… Expand

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2017

2017

We study data structures for storing a set of polygonal curves in R^d such that, given a query curve, we can efficiently retrieve… Expand

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Highly Cited

2014

Highly Cited

2014

We present a new data structure for the c-approximate near neighbor problem (ANN) in the Euclidean space. For n points in Rd, our… Expand

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Highly Cited

2012

Highly Cited

2012

Distributed frameworks are gaining increasingly widespread use in applications that process large amounts of data. One important… Expand

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Highly Cited

2011

Highly Cited

2011

Locality-sensitive hashing (LSH) is a basic primitive in several large-scale data processing applications, including nearest… Expand

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Highly Cited

2010

Highly Cited

2010

Non-metric distances are often more reasonable compared with metric ones in terms of consistency with human perceptions. However… Expand

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Highly Cited

2009

Highly Cited

2009

Fast retrieval methods are critical for large-scale and data-driven vision applications. Recent work has explored ways to embed… Expand

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Highly Cited

2009

Highly Cited

2009

We show how to learn a deep graphical model of the word-count vectors obtained from a large set of documents. The values of the… Expand

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Highly Cited

2004

Highly Cited

2004

We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem under lp norm, based on p… Expand

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Highly Cited

2003

Highly Cited

2003

Example-based methods are effective for parameter estimation problems when the underlying system is simple or the dimensionality… Expand

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