Maliciously manipulated inputs for attacking machine learning methods–in particular deep
neural networks–are emerging as a relevant issue for the security of recent artificial …

Within the topic of similarity search, all work we know assumes that search is based on a
dissimilarity space, where a query comprises a single object in the space. Here, we examine …

Permutation-based Indexing (PBI) approaches have been proven to be particularly effective
for conducting large-scale approximate metric searching. These methods rely on the idea of …

Dimensionality reduction techniques map values from a high dimensional space to one with
a lower dimension. The result is a space which requires less physical memory and has a …

Transformations of data objects into the Hamming space are often exploited to speed-up the
similarity search in metric spaces. Techniques applicable in generic metric spaces require …

Many approaches for approximate metric search rely on a permutation-based representation
of the original data objects. The main advantage of transforming metric objects into …

Abstract Approximate Nearest Neighbor (ANN) search is a prevalent paradigm for searching
intrinsically high dimensional objects in large-scale data sets. Recently, the permutation …

In the domain of approximate metric search, the Permutation-based Indexing (PBI)
approaches have been proved to be particularly suitable for dealing with large data …

Record linkage is the process of identifying records that refer to the same real-world entities
in situations where entity identifiers are unavailable. Records are linked on the basis of …

Efficient indexing and retrieval in generic metric spaces often translate into the search for
approximate methods that can retrieve relevant samples to a query performing the least …