Nowadays, neural networks act as a synonym for artificial intelligence. Present neural network models, although remarkably powerful, are inefficient both in terms of data and energy. Several ...

Meditation offers a controlled behavioral context for probing attention, arousal, and self-regulation. Rather than positioning the present work as a discovery of novel neural signatures, we analyze ...

CORAL (COnsistent RAnk Logits) and CORN (Conditional Ordinal Regression for Neural networks) are methods for ordinal regression with deep neural networks, which address the rank inconsistency issue of ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Graph-based neural networks and, specifically, message-passing neural networks (MPNNs) ...

Physics-Informed Neural Networks (PINN) emerged as a powerful tool for solving scientific computing problems, ranging from the solution of Partial Differential Equations to data assimilation tasks.

The interplay between data symmetries and network architecture is key for efficient learning in neural networks. Convolutional neural networks perform well in image recognition by exploiting the ...

Despite over 300 y of effort, no solutions exist for predicting when a general planetary configuration will become unstable. We introduce a deep learning architecture to push forward this problem for ...

Here we highlight work done on the large width limit of neural networks by the creators of the Neural Tangents library, and other close collaborators within Google. Wide Neural Networks of Any Depth ...