title: Deep processing of structured data
abstract: We construct a general unified framework for learning representation of structured
  data, i.e. data which cannot be represented as the fixed-length vectors (e.g. sets,
  graphs, texts or images of varying sizes). The key factor is played by an intermediate
  network called SAN (Set Aggregating Network), which maps a structured
  object to a fixed length vector in a high dimensional latent space. Our main theoretical
  result shows that for sufficiently large dimension of the latent space, SAN is
  capable of learning a unique representation for every input example. Experiments
  demonstrate that replacing pooling operation by SAN in convolutional networks
  leads to better results in classifying images with different sizes. Moreover, its direct
  application to text and graph data allows to obtain results close to SOTA, by
  simpler networks with smaller number of parameters than competitive models.
authors: Łukasz Maziarka Marek Śmieja Aleksandra Nowak Jacek Tabor Łukasz Struski Przemysław Spurek
hIndex: Jacek Tabor 19