We present a multi-temporal, multi-modal remote-sensing dataset for predicting how active wildfires will spread at a resolution of 24 hours. The dataset consists of 13 607 images across 607 fire events in the United States from January 2018 to October 2021. For each fire event, the dataset contains a full time series of daily observations, containing detected active fires and variables related to fuel, topography and weather conditions. The dataset is challenging due to: a) its inputs being multi-temporal, b) the high number of 23 multi-modal input channels, c) highly imbalanced labels and d) noisy labels, due to smoke, clouds, and inaccuracies in the active fire detection.

Here, we evaluate the role of hierarchical inference and its alignment with brain function in the domain of motion perception.

In many machine learning tasks, there are commonly sources of exogenous and endogenous distribution shift, necessitating that the algorithm be retrained repeatedly over time.

Molecular dynamics (MD) is a broadly adopted technique to approximate such quantities.

Despite their success, traditional V AEs rely on continuous latent variables, which deviates sharply from the discrete nature of biological neurons.

Neural Information Processing Systems http://nips.cc/

Or alternatively, are the learned dynamics highly sensitiveto different model choices?