The application of Algorithmic Recourse in decision-making is a promising field that offers practical solutions to reverse unfavorable decisions.
Conversation agents fueled by Large Language Models (LLMs) are providing a new way to interact with visual data.
There is growing interest in discovering interpretable, closed-form equations for subgrid-scale (SGS) closures/parameterizations of complex processes in Earth system.
We consider the prediction of the Hamiltonian matrix, which finds use in quantum chemistry and condensed matter physics.
Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation.
We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean.
Due to the significant increase in the size of spatial data, it is essential to use distributed parallel processing systems to efficiently analyze spatial data.
The combination of both should provide new insights for deep learning based image diagnosis.
In this work, we propose a novel activation mechanism aimed at establishing layer-level activation (LayerAct) functions.
To avoid the potential artifacts and drive the distribution of the network output close to the natural one, we reversely take synthetic images as input while the real face as reliable supervision during the training stage of face swapping.