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Found 4 papers, 1 papers with code
Anomaly Detection in Beehives: An Algorithm Comparison
Sensor-equipped beehives allow monitoring the living conditions of bees.
Anomaly Detection in Beehives using Deep Recurrent Autoencoders
Precision beekeeping allows to monitor bees' living conditions by equipping beehives with sensors.
SimLoss: Class Similarities in Cross Entropy
One common loss function in neural network classification tasks is Categorical Cross Entropy (CCE), which punishes all misclassifications equally.
MapLUR: Exploring a new Paradigm for Estimating Air Pollution using Deep Learning on Map Images
In this paper, we advocate a paradigm shift for LUR models: We propose the Data-driven, Open, Global (DOG) paradigm that entails models based on purely data-driven approaches using only openly and globally available data.
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