论文标题
代表空间轨迹作为分布
Representing Spatial Trajectories as Distributions
论文作者
论文摘要
我们为空间轨迹介绍了一个代表学习框架。我们将轨迹的部分观察结果表示为学习的潜在空间中的概率分布，这表征了轨迹未观察到的部分的不确定性。我们的框架使我们能够从任何连续的时间点从轨迹中获取样品，包括插值和推断。我们的灵活方法支持直接修改轨迹的特定属性，例如其速度，并将不同的部分观察结果组合为单个表示。实验显示了我们在预测任务中基准比基线的优势。
We introduce a representation learning framework for spatial trajectories. We represent partial observations of trajectories as probability distributions in a learned latent space, which characterize the uncertainty about unobserved parts of the trajectory. Our framework allows us to obtain samples from a trajectory for any continuous point in time, both interpolating and extrapolating. Our flexible approach supports directly modifying specific attributes of a trajectory, such as its pace, as well as combining different partial observations into single representations. Experiments show our method's advantage over baselines in prediction tasks.
