650 citations · 1.4k across the 25 of their papers we have counts for
4 papers · 1 filter
Uncertainty Voting Ensemble for Imbalanced Deep Regression
Yuchang Jiang, Vivien Sainte Fare Garnot, Konrad Schindler +1
Data imbalance is ubiquitous when applying machine learning to real-world problems, particularly regression problems. If training data are imbalanced, the learning is dominated by…
Fine-grained Population Mapping from Coarse Census Counts and Open Geodata
Nando Metzger, John E. Vargas-Muñoz, Rodrigo C. Daudt +6
Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations. Unfortunately, in many countr…
TT-NF: Tensor Train Neural Fields
Anton Obukhov, Mikhail Usvyatsov, Christos Sakaridis +2
Learning neural fields has been an active topic in deep learning research, focusing, among other issues, on finding more compact and easy-to-fit representations. In this paper, we…
Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
Nico Lang, Nikolai Kalischek, John Armston +3
NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While G…