论文标题
通过梯度下降优化用户界面布局
Optimizing User Interface Layouts via Gradient Descent
论文作者
论文摘要
自动化用户界面（UI）设计过程的部分是一个长期的挑战。我们提出了一种自动化技术，以优化移动UI的布局。我们的方法将梯度下降在任务性能的神经网络模型上相对于模型的输入，以进行布局修改，从而改善预测的错误率和任务完成时间。我们首先将基于神经网络的性能预测的先前工作扩展到具有扩展的交互空间的二维移动UI。然后，我们将方法应用于两个UI，包括该模型尚未培训的方法，以发现具有显着改善预测性能的布局替代方案。最后，我们通过实验确认这些预测，显示在优化的布局中提高了高达9.2％的改进。这证明了该算法在改善布局任务性能以及其推广和改善新接口布局的能力方面的功效。
Automating parts of the user interface (UI) design process has been a longstanding challenge. We present an automated technique for optimizing the layouts of mobile UIs. Our method uses gradient descent on a neural network model of task performance with respect to the model's inputs to make layout modifications that result in improved predicted error rates and task completion times. We start by extending prior work on neural network based performance prediction to 2-dimensional mobile UIs with an expanded interaction space. We then apply our method to two UIs, including one that the model had not been trained on, to discover layout alternatives with significantly improved predicted performance. Finally, we confirm these predictions experimentally, showing improvements up to 9.2 percent in the optimized layouts. This demonstrates the algorithm's efficacy in improving the task performance of a layout, and its ability to generalize and improve layouts of new interfaces.
