Graph neural networks (GNNs) have demonstrated strengths ... Capsule eliminates the I/O overhead between the CPU and GPU during the backpropagation process by using graph partitioning and pruning ...

exploring the newly released TF-GNN (TensorFlow Graph Neural Networks) library. To manage the computational costs associated with creating GraphTensors, we pre-process all data, saving intermediate ...

There are many ways to train a neural network. By far the most common neural network training technique (but not necessarily the best) is to use what's called the back-propagation algorithm ... every ...

In the realm of artificial intelligence and machine learning, neural networks have proven to be a powerful tool for solving complex problems. These networks, inspired by the workings of the human ...

Neural networks made from photonic chips can be trained using on-chip backpropagation – the most widely used approach to training neural networks, according to a new study. The findings pave the ...

A technical paper titled “Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks” was published by researchers ... we generate one of the largest computational defect datasets ...