Neurosymbolic Programming
Neurosymbolic Programming
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DOI：
10.1561/2500000049
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Swarat Chaudhuri;Kevin Ellis;Oleksandr Polozov;Rishabh Singh;Armando Solar-Lezama;Yisong Yue
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文献类型：
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作者：
Swarat Chaudhuri;Kevin Ellis;Oleksandr Polozov;Rishabh Singh;Armando Solar-Lezama;Yisong Yue
我们调查了关于神经合理编程的最新工作，这是一个桥接深度学习和程序合成领域的新兴领域。模块除了符号原语，并使用符号搜索和基于梯度的优化诱导。与神经网络相比，神经肯定的表示也更容易解释和正式验证。 ，Armando Solarlezama和Yisong Yue（2021），“神经标记程序”，编程语言中的基金会和趋势®：第7卷，第3期，第158-243页。记录的版本可在以下网址获得：http：//dx.doi.org/10.1561/2500000049
We survey recent work on neurosymbolic programming, an emerging area that bridges the areas of deep learning and program synthesis. Like in classic machine learning, the goal here is to learn functions from data. However, these functions are represented as programs that can use neural modules in addition to symbolic primitives and are induced using a combination of symbolic search and gradient-based optimization. Neurosymbolic programming can offer multiple advantages over end-to-end deep learning. Programs can sometimes naturally represent long-horizon, procedural tasks that are difficult to perform using deep networks. Neurosymbolic representations are also, commonly, easier to interpret and formally verify than neural networks. The restrictions of a programming language can serve as a form of regularization and lead to more generalizable and data-efficient Swarat Chaudhuri, Kevin Ellis, Oleksandr Polozov, Rishabh Singh, Armando SolarLezama and Yisong Yue (2021), “Neurosymbolic Programming”, Foundations and Trends® in Programming Languages: Vol. 7, No. 3, pp 158–243. DOI: 10.1561/2500000049. ©2021 S. Chaudhuri et al. The version of record is available at: http://dx.doi.org/10.1561/2500000049