1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Process-Guided Concept Bottleneck Model
Reza M. Asiyabi, SEOSAW Partnership, Steven Hancock +1
Concept Bottleneck Models (CBMs) improve the explainability of black-box Deep Learning (DL) by introducing intermediate semantic concepts. However, standard CBMs often overlook dom…
cs.LG2024★ 1 cited
UFLUX v2.0: A Process-Informed Machine Learning Framework for Efficient and Explainable Modelling of Terrestrial Carbon Uptake
Wenquan Dong, Songyan Zhu, Jian Xu +6
Gross Primary Productivity (GPP), the amount of carbon plants fixed by photosynthesis, is pivotal for understanding the global carbon cycle and ecosystem functioning. Process-based…
cs.CV2023★ 1 cited
Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data
Man Chen, Wenquan Dong, Hao Yu +5
The integration of multisource remote sensing data and deep learning models offers new possibilities for accurately mapping high spatial resolution forest height. We found that GED…