NEUROSYMBOLIC

In almost every field of science, it is now possible to capture large amounts of data. This has led machine learning to play an increasingly important role in scientific discovery, for example sifting through large amounts of data to identify interesting events. But modern machine learning techniques are less well suited for the critical tasks of devising hypotheses consistent with the data or imagining new experiments to test those hypotheses... Main:Slide27 The goal of our project is to develop new learning techniques that can help automate this process of generating scientific theories from data. In particular, we are working to develop methods for learning neurosymbolic models that combine neural elements capable of identifying complex patterns in data with symbolic constructs that are able to represent higher level concepts. Our approach is based on the observation that programming languages provide a uniquely expressive formalism to describe complex models. Our aim is therefore..