论文标题
可区分的编程：深度学习的概括，表征和局限性
Differentiable programming: Generalization, characterization and limitations of deep learning
论文作者
论文摘要
在过去的几年中，深度学习模型已成功地应用于几个认知任务中。这些模型最初是受神经科学的启发，是可区分程序的特定示例。在本文中，我们定义和激励可区分的编程，并指定一些程序特征，使我们能够将问题的结构纳入可区分的程序中。我们分析了不同类型的可区分程序，从更通用到更具体的程序，并评估图数据集的特定问题，其结构和知识，使用这些特征与几个可区分程序。最后，我们讨论深度学习和可区分程序的一些内在局限性，这是推进人工智能的关键挑战，然后分析可能的解决方案
In the past years, deep learning models have been successfully applied in several cognitive tasks. Originally inspired by neuroscience, these models are specific examples of differentiable programs. In this paper we define and motivate differentiable programming, as well as specify some program characteristics that allow us to incorporate the structure of the problem in a differentiable program. We analyze different types of differentiable programs, from more general to more specific, and evaluate, for a specific problem with a graph dataset, its structure and knowledge with several differentiable programs using those characteristics. Finally, we discuss some inherent limitations of deep learning and differentiable programs, which are key challenges in advancing artificial intelligence, and then analyze possible solutions
