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Found 5 papers, 2 papers with code
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
In order to encourage broader adoption of evidential deep learning in Earth System Science, we have developed a new Python package, MILES-GUESS (https://github. com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning.
Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management
When both HRRR and VIIRS retrievals were not used as model inputs, the performance dropped significantly.
Mimicking non-ideal instrument behavior for hologram processing using neural style translation
With an ML model trained to predict particle locations and shapes on the stylized data sets, we observed comparable performance on both simulated and real holograms, obviating the need to perform manual labeling.
Neural network processing of holographic images
HOLODEC, an airborne cloud particle imager, captures holographic images of a fixed volume of cloud to characterize the types and sizes of cloud particles, such as water droplets and ice crystals.
Learning retrosynthetic planning through self-play
The problem of retrosynthetic planning can be framed as one player game, in which the chemist (or a computer program) works backwards from a molecular target to simpler starting materials though a series of choices regarding which reactions to perform.
