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Found 10 papers, 1 papers with code
GrINd: Grid Interpolation Network for Scattered Observations
GrINd offers a promising approach for forecasting physical systems from sparse, scattered observational data, extending the applicability of deep learning methods to real-world scenarios with limited data availability.
Global Vegetation Modeling with Pre-Trained Weather Transformers
We demonstrate that leveraging pre-trained weather models improves the NDVI estimates compared to learning an NDVI model from scratch.
TaylorPDENet: Learning PDEs from non-grid Data
Modeling data obtained from dynamical systems has gained attention in recent years as a challenging task for machine learning models.
DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data
The dataset focuses on predicting the evolution of a dynamical system from low-resolution, unstructured measurements.
Semi-unsupervised Learning for Time Series Classification
Due to its chaotic nature and massive amounts of datapoints, timeseries are hard to label manually.
Open ERP System Data For Occupational Fraud Detection
Recent estimates report that companies lose 5% of their revenue to occupational fraud.
NeuralPDE: Modelling Dynamical Systems from Data
Many physical processes such as weather phenomena or fluid mechanics are governed by partial differential equations (PDEs).
Anomaly Detection in Beehives: An Algorithm Comparison
Sensor-equipped beehives allow monitoring the living conditions of bees.
Deep Learning for Climate Model Output Statistics
Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation.
Anomaly Detection in Beehives using Deep Recurrent Autoencoders
Precision beekeeping allows to monitor bees' living conditions by equipping beehives with sensors.
