Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

The German sensor-maker Leuze claims that it has been able to cut measurement errors in demanding industrial applications by ...

Imagine watching a conversation between brain cells, seeing chemical messages pass from one neuron to another. Scientists can ...

It’s been ten years since AlexNet, a deep learning convolutional neural network (CNN) model running on GPUs, displaced more traditional vision processing algorithms to win the ImageNet Large Scale ...

AI has successfully been applied in many areas of science, advancing technologies like weather prediction and protein folding ...

Researchers have developed a fiber neural network system that performs intelligent processing of optical communication signals directly in the light domain. This approach integrates optical ...

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...

Neural and computational evidence reveals that real-world size is a temporally late, semantically grounded, and hierarchically stable dimension of object representation in both human brains and ...

eSpeaks' Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...

Confused about cost functions in neural networks? In this video, we break down what cost functions are, why they matter, and ...