We present the first provably sublinear time hashing algorithm for approximate\emph
{Maximum Inner Product Search}(MIPS). Searching with (un-normalized) inner product as …

In this article, we give an overview of efficient algorithms for the approximate and exact
nearest neighbor problem. The goal is to preprocess a dataset of objects (eg, images) so …

This lecture note describes a technique known as locality-sensitive hashing (LSH) that
allows one to quickly find similar entries in large databases. This approach belongs to a …

Current deep learning architectures are growing larger in order to learn from complex
datasets. These architectures require giant matrix multiplication operations to train millions …

Large software projects contain significant code duplication, mainly due to copying and
pasting code. Many techniques have been developed to identify duplicated code to enable …

While minwise hashing is promising for large-scale learning in massive binary data, the
preprocessing cost is prohibitive as it requires applying (eg,) $ k= 500$ permutations on the …

Software security vulnerabilities are discovered on an almost daily basis and have caused
substantial damage. Aiming at supporting early detection and resolution for them, we have …

Recent research results suggest a need for code clone management. In this paper, we
introduce JSync, a novel clone management tool. JSync provides two main functions to …

MinHash and SimHash are the two widely adopted Locality Sensitive Hashing (LSH)
algorithms for large-scale data processing applications. Deciding which LSH to use for a …

Recently it was shown that the problem of Maximum Inner Product Search (MIPS) is efficient
and it admits provably sub-linear hashing algorithms. Asymmetric transformations before …