论文标题
HypperSter：假设转向和数据扰动，以深度学习顺序预测
HypperSteer: Hypothetical Steering and Data Perturbation in Sequence Prediction with Deep Learning
论文作者
论文摘要
深度复发的神经网络（RNN）继续在使用时间事件序列的预测决策中找到成功。最近的研究表明，视觉分析在解释真实世界应用深度学习模型中的重要性和实用性。但是，非常有限的工作使与深度学习模型的互动和指导从业者可以针对所需的预测结果形成假设，尤其是对于序列预测。具体而言，没有现有的工作解决了沿不同时间步长以进行序列结果预测的何种分析和价值扰动。我们提出了一种模型不可吻合的视觉分析工具Hypperster，该工具引导假设测试，并允许用户互动地进行序列预测。我们展示了Hypperster如何帮助指导患者数据以达到所需的治疗结果，并讨论如何作为其他实际情况的全面解决方案。
Deep Recurrent Neural Networks (RNN) continues to find success in predictive decision-making with temporal event sequences. Recent studies have shown the importance and practicality of visual analytics in interpreting deep learning models for real-world applications. However, very limited work enables interactions with deep learning models and guides practitioners to form hypotheticals towards the desired prediction outcomes, especially for sequence prediction. Specifically, no existing work has addressed the what-if analysis and value perturbation along different time-steps for sequence outcome prediction. We present a model-agnostic visual analytics tool, HypperSteer, that steers hypothetical testing and allows users to perturb data for sequence predictions interactively. We showcase how HypperSteer helps in steering patient data to achieve desired treatment outcomes and discuss how HypperSteer can serve as a comprehensive solution for other practical scenarios.
