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Work in Progress : Synthesis of Differentiable Functional Programs for Lifelong Learning 1

Differentiable programming languages (Paszke et al., 2017; Bosnjak et al., 2017; Gaunt et al., 2016; Bunel et al., 2016) have recently emerged as a powerful approach to representing architectures with input-dependent structure, such as deep networks over trees (Socher et al., 2013; Allamanis et al., 2017) and graphs (Li et al., 2016; Kipf & Welling, 2017). Being able to induce differentiable programs purely from data can have tremendous value in many applications. Unfortunately, inferring the structure of differentiable programs is a fundamentally hard problem. While some recent approaches (Gaunt et al., 2017; 2016) partially solve this problem, a detailed hand-written template of the program is required for even the simplest tasks.