论文标题
顺序数据中的歧义：通过复发模型预测不确定的未来
Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models
论文作者
论文摘要
模棱两可在许多机器学习任务中固有地存在，但尤其是对于很少考虑的顺序模型，因为大多数仅输出单个预测。在这项工作中，我们提出了多个假设预测（MHP）模型的扩展，以处理具有顺序数据的模棱两可的预测，这是特别重要的，因为多个未来同样可能同样可能。我们的方法可以应用于最常见的复发体系结构，并可以与任何损失功能一起使用。此外，我们为模棱两可的问题引入了一种新颖的指标，该指标更适合说明不确定性，并与我们在存在多个标签的情况下对正确性的直观理解相吻合。我们在几个实验以及处理时间序列数据的各种任务上测试了我们的方法，例如轨迹预测和操纵预测，取得了令人鼓舞的结果。
Ambiguity is inherently present in many machine learning tasks, but especially for sequential models seldom accounted for, as most only output a single prediction. In this work we propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data, which is of special importance, as often multiple futures are equally likely. Our approach can be applied to the most common recurrent architectures and can be used with any loss function. Additionally, we introduce a novel metric for ambiguous problems, which is better suited to account for uncertainties and coincides with our intuitive understanding of correctness in the presence of multiple labels. We test our method on several experiments and across diverse tasks dealing with time series data, such as trajectory forecasting and maneuver prediction, achieving promising results.
