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DiConStruct: Causal Concept-based Explanations through Black-Box Distillation
Lastly, explanation methods should be efficient and not compromise the performance of the predictive task.
LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering
Reviewing these cases is a cumbersome and complex task that requires analysts to navigate a large network of financial interactions to validate suspicious movements.
Modeling the geospatial evolution of COVID-19 using spatio-temporal convolutional sequence-to-sequence neural networks
Europe was hit hard by the COVID-19 pandemic and Portugal was one of the most affected countries, having suffered three waves in the first twelve months.
Spatio-Temporal Forecasting With Gridded Remote Sensing Data Using Feed-Backward Decoding
We present a novel deep learning approach for spatio-temporal forecasting with remote sensing data, extending a previous model named Spatio-Temporal Convolutional Sequence to Sequence Network (STConvS2S) in several directions.
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