Search results for "Learned Data Structures"
showing 2 items of 2 documents
On the Suitability of Neural Networks as Building Blocks for the Design of Efficient Learned Indexes
2022
With the aim of obtaining time/space improvements in classic Data Structures, an emerging trend is to combine Machine Learning techniques with the ones proper of Data Structures. This new area goes under the name of Learned Data Structures. The motivation for its study is a perceived change of paradigm in Computer Architectures that would favour the use of Graphics Processing Units and Tensor Processing Units over conventional Central Processing Units. In turn, that would favour the use of Neural Networks as building blocks of Classic Data Structures. Indeed, Learned Bloom Filters, which are one of the main pillars of Learned Data Structures, make extensive use of Neural Networks to improve…
Learned Sorted Table Search and Static Indexes in Small Model Space
2022
Machine Learning Techniques, properly combined with Data Structures, have resulted in Learned Static Indexes, innovative and powerful tools that speed-up Binary Search, with the use of additional space with respect to the table being searched into. Such space is devoted to the ML model. Although in their infancy, they are methodologically and practically important, due to the pervasiveness of Sorted Table Search procedures. In modern applications, model space is a key factor and, infact, a major open question concerning this area is to assess to whatextent one can enjoy the speed-up of Learned Indexes while using constant or nearly constant space models.We address it here by (a) introducing…