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Found 4 papers, 2 papers with code
Representation Learning for Appliance Recognition: A Comparison to Classical Machine Learning
On the basis of an event-based appliance recognition approach, we evaluate seven different classification models: a classical machine learning approach that is based on a hand-crafted feature extraction, three different deep neural network architectures for automated feature extraction on raw waveform data, as well as three baseline approaches for raw data processing.
So2Sat POP -- A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale
Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-technical supply.
Generative modeling of spatio-temporal weather patterns with extreme event conditioning
Deep generative models are increasingly used to gain insights in the geospatial data domain, e. g., for climate data.
Training Auto-encoder-based Optimizers for Terahertz Image Reconstruction
Extracting the interpretable and physically meaningful parameters for such applications, however, requires solving an inverse problem in which a model function determined by these parameters needs to be fitted to the measured data.
