Synthesis of Differentiable Functional Programs for Lifelong Learning


Jun 02, 2018 ICML 2018 Workshop NAMPI Blind Submission readers: everyone
  • Abstract: We present a {\em neurosymbolic approach} to the lifelong learning of algorithmic tasks that mix perception and procedural reasoning. Reusing high-level concepts across domains and learning complex procedures are key challenges in lifelong learning. We show that a combination of gradient-based learning and {symbolic program synthesis} can be a more effective response to these challenges than purely neural methods. Our approach, called \system, represents neural networks as strongly typed, end-to-end differentiable functional programs that use symbolic higher-order combinators to compose a library of neural functions. Our learning algorithm consists of: (1) a symbolic program synthesizer that performs a type-directed search over parameterized programs, and decides on the library functions to reuse, and the architectures to combine them, while learning a sequence of tasks; and (2) a neural module that trains these programs using stochastic gradient descent. Our experiments show that \system transfers high-level concepts more effectively than traditional transfer learning and progressive neural networks.
  • Keywords: program synthesis, lifelong learning
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