Yisong Yue (Caltech)/ Swarat Chaudhuri (UT Austin) / Jennifer Sun (Caltech)

This part overviews the design of neurosymbolic architectures and their training. We will begin with an overview of conventional deep learning (i.e., purely neural architectures). Afterwards, we will introduce the concept of a domain specific language (DSL), which can include both symbolic and neural primitives. Any program or architecture can be designed using primitives from this DSL. We will then construct a few explicit neurosymbolic architectures and train their continuous parameters using gradient-based learning.