基于栈扩充递归网的数学模式推断
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets
摘要：
尽管最近在机器学习方面取得了一些成就，但我们离实现真正的人工智能还很远。在本文中，我们讨论了标准深度学习方法的局限性，并表明其中一些局限性可以通过学习如何以结构化的方式增加模型的复杂性来克服。具体来说，我们研究了最简单的序列预测问题，这些问题超出了标准递归网络的可学习范围，算法生成的序列只能通过具有计数和记忆序列能力的模型来学习。我们表明，一些基本的算法可以从序列数据中学习，使用与可训练记忆相关的递归网络。
英文摘要：
Despite the recent achievements in machine learning, we are still very far from achieving real artificial intelligence. In this paper, we discuss the limitations of standard deep learning approaches and show that some of these limitations can be overcome by learning how to grow the complexity of a model in a structured way. Specifically, we study the simplest sequence prediction problems that are beyond the scope of what is learnable with standard recurrent networks, algorithmically generated sequences which can only be learned by models which have the capacity to count and to memorize sequences. We show that some basic algorithms can be learned from sequential data using a recurrent network associated with a trainable memory.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
