To address this issue, high-order structure preserving graph neural network (HOSP-GNN) can deeply explore the rich samples structure of the different meta-tasks to predict the label of the queried ...

Neural networks have revolutionized the fields of artificial intelligence (AI) and machine learning by providing a flexible, and scalable, means to solve complex, and traditionall ...

However, the existing graph neural network framework often uses methods based on spatial domain or spectral domain to capture network structure features. This process captures the local structural ...

This innovative optical system encodes data as holograms, utilizing neural networks for decryption, paving the way for ...

The textbook meaning of an artificial neural network (ANN) is a deep learning model made up of neurons that emulate the structure of the human brain. These neurons are designed to mimic the way nerve ...

Finally, the two different embeddings are integrated. The probability that the interconnected nodes in the sparse graph belong to the same type decreases with the increase of distance, so the output ...

For their experiments with artificial intelligence, Dr. Steffen Werner and his colleague Michael Coughlan drew inspiration from the ways animals ...

Neural activity in the cortical amygdala determines whether mice engage in aggressive or pro-social behavior, according to a ...

Learn More A new neural-network architecture developed by researchers at Google might solve one of the great challenges for large language models (LLMs): extending their memory at inference time ...

This is a lightweight repository of bayesian neural network for PyTorch. @article{lee2022graddiv, title={Graddiv: Adversarial robustness of randomized neural networks via gradient diversity ...

By combining AI with holographic encryption, scientists have developed an ultra-secure data protection system. Their method ...

Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural ...