As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...

For example, a Convolutional Neural Network (CNN) trained on thousands of radar echoes can recognize the unique spatial signature of a small metallic fragment, even when its signal is partially masked ...

Encoding individual behavioral traits into a low-dimensional latent representation enables the accurate prediction of ...

The Indonesian Throughflow carries both warm water and fresh water from the Pacific into the Indian Ocean. As the only ...

The researchers also found that combining data from both the Maluku Strait and the nearby Halmahera Strait further improved predictions of system-wide conditions.

Early-2026 explainer reframes transformer attention: tokenized text becomes Q/K/V self-attention maps, not linear prediction.

In this article, the authors investigated how the brain anticipates sequences of potential sensory events, using temporal predictability to enhance perception. To do so, they combined a tone detection ...

Introduction: As the number of Internet of Things (IoT) devices grows quickly, cyber threats are becoming more complex and increasingly sophisticated; thus, we need a more robust network security ...

in this video, we will understand what is Recurrent Neural Network in Deep Learning. Recurrent Neural Network in Deep Learning is a model that is used for Natural Language Processing tasks. It can be ...

1 KNDS Deutschland GmbH & Co. KG, Munich, Germany 2 Institute for Software Technology, University of the Bundeswehr Munich, Neubiberg, Germany Artificial intelligence (AI) has emerged as a ...

Abstract: Shifts in data distribution across time can strongly affect early classification of time-series data. When decoding behavior from neural activity, early detection of behavior may help in ...