HOUDINI: Lifelong Learning as Program Synthesis
HOUDINI: Lifelong Learning as Program Synthesis
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发表时间：
2018-03
期刊：
影响因子：
3.2
通讯作者：
Lazar Valkov;Dipak Chaudhari;Akash Srivastava;Charles Sutton;Swarat Chaudhuri
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作者：
Lazar Valkov;Dipak Chaudhari;Akash Srivastava;Charles Sutton;Swarat Chaudhuri
我们提出了一个终身学习的算法框架，这些算法将感知和渐进式推理融合在一起。对这些挑战的搜索比纯粹的神经方法更有效算法包括：（1）符号程序合成器，该综合器对参数化程序进行类型定向的搜索，并决定库函数重复使用，以及在学习任务序列的同时结合起来使用随机梯度下降来训练这些程序的神经模块，我们在三个基准上评估了Houdini，这些基准将感知与计数，总结和最短的实验计算的算法任务相结合。学习和渐进的神经网络，并且网络的键入表示形式显着加速了搜索。
We present a neurosymbolic framework for 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 program synthesis approach that combines gradient descent with combinatorial search over programs can be a more effective response to these challenges than purely neural methods. Our framework, called HOUDINI, represents neural networks as strongly typed, 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. We evaluate HOUDINI on three benchmarks that combine perception with the algorithmic tasks of counting, summing, and shortest-path computation. Our experiments show that HOUDINI transfers high-level concepts more effectively than traditional transfer learning and progressive neural networks, and that the typed representation of networks significantly accelerates the search.