Multi-Task Learning for Sparsely-Labeled Time Series: A Case Study on Cold-Hardiness Modeling
Multi-task RNN architectures pooling sparse cultivar data improve grape cold-hardiness and budbreak prediction over single-task and scientific baselines.
Researchers apply recurrent neural networks to daily grape cold-hardiness prediction from weather time series, where per-cultivar labels are temporally sparse and limited. They design multiple multi-task learning architectures that treat cultivars as tasks and evaluate them in both MTL and transfer learning settings. Certain architectures consistently outperform single-task learning and state-of-the-art scientific models, and a single MTL model jointly learning cold hardiness and budbreak improves accuracy on both tasks.
- RNNs predict daily grape cold hardiness from weather time series
- Multi-task architectures treat cultivars as tasks to pool sparse labels
- Selected architectures consistently beat single-task and scientific baselines
- Joint cold-hardiness and budbreak MTL model improves both tasks
From the original article
1 min read at arxiv.orgWe present a real-world case study of multi-task learning (MTL) for temporal process modeling from limited data with temporally sparse labels. Specifically, we investigate multi-task learning for the important agricultural problem of predicting grape cold hardiness, which is the temperature at which lethal freezing occurs.
By Aseem Saxena · arxiv.org
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