2 citations · 2 across the 7 of their papers we have counts for
7 papers
Multi-Task Learning for Sparsely-Labeled Time Series: A Case Study on Cold-Hardiness Modeling
Aseem Saxena, Paola Pesántez-Cabrera, Jonathan Magby +2
We 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-ta…
Cross-Regional Grapevine Cold Hardiness Prediction via Learned Multimodal Latent Representations
William Solow, Paola Pesantez-Cabrera, Markus Keller +3
Accurate daily predictions of cold hardiness in woody plants are critical in regions where freezing temperatures can damage dormant buds and reduce seasonal yield. Existing biophys…
A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning
William Solow, Paola Pesantez-Cabrera, Markus Keller +3
Accurate prediction of crop states (e.g., phenology stages and cold hardiness) is essential for timely farm management decisions such as irrigation, fertilization, and canopy manag…
Budgeted Online Active Learning with Expert Advice and Episodic Priors
Kristen Goebel, William Solow, Paola Pesantez-Cabrera +2
This paper introduces a novel approach to budgeted online active learning from finite-horizon data streams with extremely limited labeling budgets. In agricultural applications, su…
Transfer Learning via Auxiliary Labels with Application to Cold-Hardiness Prediction
Kristen Goebel, Paola Pesantez-Cabrera, Markus Keller +1
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…
Multi-Task Learning for Budbreak Prediction
Aseem Saxena, Paola Pesantez-Cabrera, Rohan Ballapragada +2
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…