抽象深度学习模型开发的可视化编程范式
A Visual Programming Paradigm for Abstract Deep Learning Model Development
摘要：
深度学习是计算机科学中发展最快的技术之一，具有大量的应用。但这种前所未有的增长迄今为止仅限于深度学习专家的消费。主要的挑战是学习编程库的学习曲线陡峭，以及缺乏直观的系统，使非专家能够使用深度学习。为了实现这一目标，我们研究了无代码范式设计深度学习模型的有效性。特别是，一个可视化的拖放界面相比，传统的编程和替代的可视化编程范式更有效。我们对不同专业水平的用户进行研究，以衡量不同编程范式的入门级障碍和开发人员负载。我们获得了系统可用性量表（SUS）的90和NASA的任务负荷指数（TLX）得分为21的建议的可视化编程相比，68和52，分别为传统的编程方法。
英文摘要：
Deep learning is one of the fastest growing technologies in computer science with a plethora of applications. But this unprecedented growth has so far been limited to the consumption of deep learning experts. The primary challenge being a steep learning curve for learning the programming libraries and the lack of intuitive systems enabling non-experts to consume deep learning. Towards this goal, we study the effectiveness of a no-code paradigm for designing deep learning models. Particularly, a visual drag-and-drop interface is found more efficient when compared with the traditional programming and alternative visual programming paradigms. We conduct user studies of different expertise levels to measure the entry level barrier and the developer load across different programming paradigms. We obtain a System Usability Scale (SUS) of 90 and a NASA Task Load index (TLX) score of 21 for the proposed visual programming compared to 68 and 52, respectively, for the traditional programming methods.
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。
