Largeur | Data scientist, the craft of the century [In French]

We’ve developed a smart solution for wind tunnel testing that learns as it works, providing accurate results faster. It provides an accurate mean flow field and turbulence field reconstruction while shortening the sampling time.

Can we extend the concept of word embeddings to any collection of items, possibly unordered? More precisely, can we learn representations from item sets, such as the product baskets in online retail or music playlists on streaming platforms?

Not all datasets are images and we need architectures that adapt to other types of data, encoding both domain specific knowledge and data specific characteristics. For instance, at the SDSC, we deal with spherical data, i.e. curved images on a sphere, but without clear borders and arbitrary orientation.