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Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
TLDR
We present an algorithm for the c-approximate nearest neighbor problem in a d-dimensional Euclidean space, achieving query time of O\left( {dn^{1/c^2 + o(1)} } \right). Expand
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Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
  • A. Andoni, P. Indyk
  • Mathematics, Computer Science
  • 47th Annual IEEE Symposium on Foundations of…
  • 2006
TLDR
We present an algorithm for the c-approximate nearest neighbor problem in a d-dimensional Euclidean space, achieving query time of O(dn 1c2/+o(1)). Expand
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Practical and Optimal LSH for Angular Distance
TLDR
We show the existence of a Locality-Sensitive Hashing (LSH) family for the angular distance that yields an approximate Near Neighbor Search algorithm with the asymptotically optimal running time exponent. Expand
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Optimal Data-Dependent Hashing for Approximate Near Neighbors
TLDR
We show an optimal data-dependent hashing scheme for the approximate near neighbor problem. Expand
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Learning Polynomials with Neural Networks
TLDR
We study the effectiveness of learning low degree polynomials using neural networks by the gradient descent method. Expand
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Beyond Locality-Sensitive Hashing
TLDR
We present a new data structure for the c-approximate near neighbor problem (ANN) in the Euclidean space, where ρ ≤ 7/(8c2) + O(1/c3) + oc(1). Expand
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Parallel algorithms for geometric graph problems
TLDR
We give algorithms for geometric graph problems in the modern parallel models such as MapReduce. Expand
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Optimal Hashing-based Time-Space Trade-offs for Approximate Near Neighbors
TLDR
We show tight upper and lower bounds for time-space trade-offs for the $c$-Approximate Near Neighbor Search problem. Expand
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Nearest neighbor search : the old, the new, and the impossible
TLDR
We give a new algorithm for the approximate NN problem in the d-dimensional Euclidean space in the class of hashing algorithms. Expand
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Testing k-wise and almost k-wise independence
TLDR
In this work, we consider the problems of testing whether adistribution over 0,1<sup>n</sup>) is <i>k</i>-wise (resp. Expand
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