PAPIs Latam 2019

Attention-based neural networks have recently achieved excellent performance on machine translation tasks. A 2017 NIPS paper from Google , “Attention Is All You Need”, demonstrated that attention mechanisms have the potential to replace recurrent neural networks, which are known to be computationally expensive and at times unstable. We provide a detailed example of how the same ideas can be applied to relational databases, by using attention layers which learn to condense the information present in a collection of related data tables, without the need for recurrent layers or hand-crafted features.