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Found 5 papers, 0 papers with code
Probing the limit of hydrologic predictability with the Transformer network
While the Transformer results are not higher than current state-of-the-art, we still learned some valuable lessons: (1) the vanilla Transformer architecture is not suitable for hydrologic modeling; (2) the proposed recurrence-free modification can improve Transformer performance so future work can continue to test more of such modifications; and (3) the prediction limits on the dataset should be close to the current state-of-the-art model.
Practical Frequency-Hopping MIMO Joint Radar Communications: Design and Experiment
Joint radar and communications (JRC) can realize two radio frequency (RF) functions using one set of resources, greatly saving hardware, energy and spectrum for wireless systems needing both functions.
Online Motion Style Transfer for Interactive Character Control
Motion style transfer is highly desired for motion generation systems for gaming.
Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy
Without using an ensemble or post-processor, {\delta} models can obtain a median Nash Sutcliffe efficiency of 0. 732 for 671 basins across the USA for the Daymet forcing dataset, compared to 0. 748 from a state-of-the-art LSTM model with the same setup.
From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
The behaviors and skills of models in many geosciences (e. g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration.
