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

By using a neural network-based decoupling algorithm, the team was able to resolve spectral interference within the existing system, reducing both the complexity and cost of the design.

Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...

Neural information extraction algorithms can be trained on data from a few institutions and then can be applied to data from previously unseen hospitals and clinics. This helps to reduce the burden ...

The algorithm uses supervised learning with known histopathology diagnoses (malignant and nonmalignant) as the labels for algorithm training. MIA3G is a classification deep feedforward neural network ...

Deep neural networks can solve the most challenging problems, but require abundant computing power and massive amounts of data.

The rapid advancement of artificial intelligence (AI) and machine learning systems has increased the demand for new hardware components that could speed up data analysis while consuming less power. As ...

Artificial neural networks, the underlying structure of deep learning algorithms, roughly mimic the physical structure of the human brain.