Graph neural networks help to process and analyze complex graph-structured data, unlocking new possibilities across a wide range of applications.

Neural networks come in a variety of types that can be applied to separate use cases: Convolutional neural networks: Similar to ordinary neural networks, CNNs differ in that they “make the explicit ...

Convolutional neural networks (CNNs) are a type of neural network that is designed to capture increasingly more complex features within its input data. To do this, CNNs are constructed from a ...

Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech ...

The machine learning tool is helping physicists with the daunting challenge of analyzing large but nearly empty data sets, like those from neutrino detectors or particle colliders.

This paper is concerned with a class of neutral-type neural networks with impulses and delays. By using continuation theorem due to Mawhin and constructing the appropriate Lyapunov-Krasovskii ...

Researchers have developed a new tool, bimodularity, that adds directionality to community detection in networks.