用熵驱动课程学习加速多任务移动预测，提升精度与收敛速度。
Entropy-Driven Curriculum for Multi-Task Training in Human Mobility Prediction
- 基于压缩熵构建课程，由简到繁训练轨迹预测
- 同时预测位置、距离和方向，提升模型泛化能力
- 在真实数据上实现2.92倍提速，精度达最新水平
可穿戴设备带来的大规模移动数据为深度学习驱动的人类移动预测提供了可能。然而，人类移动数据的复杂性差异导致模型训练效率低下，梯度更新不充分，易出现欠拟合。同时，仅预测下一位置会忽略距离、方向等隐含因素，影响预测效果。本文提出一种融合熵驱动课程学习与多任务学习的统一框架：通过Lempel-Ziv压缩量化轨迹可预测性，构建从简单到复杂的训练序列，加快收敛；多任务同时优化主任务（位置预测）及辅助任务（距离与方向估计），利用互补监督信号学习更真实的移动模式。在符合HuMob挑战标准的实验中，本方法在GEO-BLEU（0.354）和DTW（26.15）指标上达到当前最优性能，相比无课程学习的训练方式，收敛速度提升最高达2.92倍。
原文摘要 · Abstract (English)
The increasing availability of big mobility data from ubiquitous portable devices enables human mobility prediction through deep learning approaches. However, the diverse complexity of human mobility data impedes model training, leading to inefficient gradient updates and potential underfitting. Meanwhile, exclusively predicting next locations neglects implicit determinants, including distances and directions, thereby yielding suboptimal prediction results. This paper presents a unified training framework that integrates entropy-driven curriculum and multi-task learning to address these challenges. The proposed entropy-driven curriculum learning strategy quantifies trajectory predictability based on Lempel-Ziv compression and organizes training from simple to complex for faster convergence and enhanced performance. The multi-task training simultaneously optimizes the primary location prediction alongside auxiliary estimation of movement distance and direction for learning realistic mobility patterns, and improve prediction accuracy through complementary supervision signals. Extensive experiments conducted in accordance with the HuMob Challenge demonstrate that our approach achieves state-of-the-art performance on GEO-BLEU (0.354) and DTW (26.15) metrics with up to 2.92-fold convergence speed compared to training without curriculum learning.
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