Multi-Task Learning for Budbreak Prediction
Grapevine budbreak is a key phenological stage of seasonal development, which serves as a signal for the onset of active growth. This is also when grape plants are most vulnerable to damage from freezing temperatures. Hence, it is important for winegrowers to anticipate the day of budbreak occurrence to protect their vineyards from late spring frost events. This work investigates deep learning for budbreak prediction using data collected for multiple grape cultivars. While some cultivars have over 30 seasons of data others have as little as 4 seasons, which can adversely impact prediction accuracy. To address this issue, we investigate multi-task learning, which combines data across all cultivars to make predictions for individual cultivars. Our main result shows that several variants of multi-task learning are all able to significantly improve prediction accuracy compared to learning for each cultivar independently.
Code (0)
등록된 구현이 없습니다.
Tasks
Multi-Task LearningPredictionSimilar Papers 제목 키워드 기반
Transfer Learning via Auxiliary Labels with Application to Cold-Hardiness Prediction
Cold temperatures can cause significant frost damage to fruit crops depending on their resilience, or cold hardiness, which changes throughout the dormancy season. This has led to the development of predictive cold-hardi…
Model SelectionTransfer LearningPatient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning
Multitask deep learning has been applied to patient outcome prediction from text, taking clinical notes as input and training deep neural networks with a joint loss function of multiple tasks. However, the joint training…
PredictionDeconstructing Supertagging into Multi-Task Sequence Prediction
Supertagging is a sequence prediction task where each word is assigned a piece of complex syntactic structure called a supertag. We provide a novel approach to multi-task learning for Tree Adjoining Grammar (TAG) superta…
Multi-Task LearningPredictionTAGContrastive Multi-Task Dense Prediction
This paper targets the problem of multi-task dense prediction which aims to achieve simultaneous learning and inference on a bunch of multiple dense prediction tasks in a single framework. A core objective in design is h…
Contrastive LearningPredictionRepresentation LearningLegal Judgment Prediction via Multi-Perspective Bi-Feedback Network
The Legal Judgment Prediction (LJP) is to determine judgment results based on the fact descriptions of the cases. LJP usually consists of multiple subtasks, such as applicable law articles prediction, charges prediction,…
ArticlesPrediction