Deep learning models for tabular data are restricted to a specific table
format. Computer vision models, on the other hand, have a broader
applicability; they work on all images and can learn universal features.
This allows them to be trained on enormous corpora and have very wide
transferability and applicability. Inspired by these properties, this
work presents an architecture that aims to capture useful patterns
across arbitrary tables. The model is trained on randomly sampled
subsets of features from a table, processed by a convolutional network.
This internal representation captures feature interactions that appear
in the table. Experimental results show that the embeddings produced by
this model are useful and transferable across many commonly used machine
learning benchmarks datasets. Specifically, that using the embeddings
produced by the network as additional features, improves the performance
of a number of classifiers.