However, recent findings indicate that small, unnoticeable perturbations of graph structure can catastrophically reduce performance of even the strongest andmost popular Graph Neural Networks (GNNs).

Images produced bydiffusionmodels areincreasingly popular indigital artwork and visual marketing. However, such generated images might replicate content from existing ones andpose thechallenge ofcontent originality.

Deep neural networks are starting to show their potential for solving partial differential equations (PDEs)inavarietyofproblemdomains,includingturbulentflow,heattransfer,elastodynamics,and many more [1, 2, 3, 4, 5]. Thanks to their smooth and analytically-differentiable nature, implicit neural representations with periodic activation functions are emerging as a particularly attractive and powerful option in this context [4].

The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs).

"Gated Orthogonal Recurrent Units: On Learningto Forget".