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Found 4 papers, 1 papers with code
Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target Imputation
These results are consistent for experiments on above-ground biomass prediction in Tanzania and stem volume prediction in Norway, representing a diversity in parameters and forest types that emphasises the robustness of the approach.
Simpler is better: spectral regularization and up-sampling techniques for variational autoencoders
The current solution to avoid this is to either replace transposed convolutions with bilinear up-sampling or add a spectral regularization term in the generator.
On the potential of sequential and non-sequential regression models for Sentinel-1-based biomass prediction in Tanzanian miombo forests
We develop a traditional, parametric regression model and alternative non-parametric models for this stage.
Decision Support Systems in Fisheries and Aquaculture: A systematic review
We provide an analysis of the research done in the studies collected.
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