Emotion recognition plays an important role in understanding human cognition and behavior, and has potential applications in brain-computer interfaces and mental health monitoring.

A graph convolutional network with incremental learning improves network anomaly detection across three benchmark datasets by ...

A systematic survey of 22 studies finds that no published fraud-detection system simultaneously combines graph learning, edge ...

How would you improve schools? By The Learning Network How much confidence do you have in U.S. institutions? By The Learning Network A new collection of graphs, maps and charts organized by topic and ...

The exponential function continuesYou may be tired of hearing that AI intelligence is growing exponentially. However, what ...

Do graph neural networks help detect illicit transactions, and do any of these models keep working when fraud patterns change? This project compares five models on the Elliptic Bitcoin dataset with a ...

Accurate molecular energy prediction relies on representations of atomic species and three-dimensional structure. However, repeated aggregation over local neighborhoods can make it difficult for ...

Decoding failures that jeopardise next generation encryption are now reduced by 63. 7% thanks to a new neural network ...

Last time, we learned about loss functions.AI turns the difference between a prediction and the correct answer into a ...

Baldi, meanwhile, is being recognized for his early contributions to the field. He was named the recipient of the 2027 IEEE ...

GTFormer proposes replacing Transformer self-attention with a hierarchical graph neural network, achieving zero-shot length ...

At the core of the Bitcoin network and other cryptocurrencies is a strong tension. On the one hand, these cryptocurrencies were designed to provide the maximum individual freedom and anonymity. On the ...