论文标题
神经抽象推理器
Neural Abstract Reasoner
论文作者
论文摘要
抽象的推理和逻辑推断是神经网络的困难问题，但对于它们在高度结构化领域中的适用性至关重要。在这项工作中，我们证明了一种众所周知的技术（例如光谱正则化）可以显着提高神经学习者的能力。我们介绍了神经抽象推理器（NAR），这是一种能够学习和使用抽象规则的内存增强体系结构。我们表明，在接受频谱正则化培训时，NAR在抽象和推理语料库上获得了$ 78.8 \％$的精度，比最知名的人类手工制作的符号求解器提高了4次的性能。我们为基于理论泛化范围和所罗门诺夫的归纳推理理论在抽象推理领域中光谱正则化的影响提供了一些直觉。
Abstract reasoning and logic inference are difficult problems for neural networks, yet essential to their applicability in highly structured domains. In this work we demonstrate that a well known technique such as spectral regularization can significantly boost the capabilities of a neural learner. We introduce the Neural Abstract Reasoner (NAR), a memory augmented architecture capable of learning and using abstract rules. We show that, when trained with spectral regularization, NAR achieves $78.8\%$ accuracy on the Abstraction and Reasoning Corpus, improving performance 4 times over the best known human hand-crafted symbolic solvers. We provide some intuition for the effects of spectral regularization in the domain of abstract reasoning based on theoretical generalization bounds and Solomonoff's theory of inductive inference.
