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Found 3 papers, 0 papers with code
Predicting Wind-Driven Spatial Deposition through Simulated Color Images using Deep Autoencoders
We demonstrate this approach on images of spatial deposition from a pollution source, where the encoder compresses the dimensionality to 0. 02% of the original size, and the full predictive model performance on test data achieves a normalized root mean squared error of 8%, a figure of merit in space of 94% and a precision-recall area under the curve of 0. 93.
Deep Convolutional Autoencoders as Generic Feature Extractors in Seismological Applications
The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing.
Improving seasonal forecast using probabilistic deep learning
We apply this probabilistic forecast methodology to quantify the impacts of initialization errors and model formulation deficiencies in a dynamical seasonal forecasting system.
