In electromagnetic inversion, Convolutional Neural Networks (CNN) can deeply mine data features through supervised learning and obtain effective inversion models through training, thereby overcoming ...

MicroCloud Hologram Inc. announces a noise-resistant Deep Quantum Neural Network architecture, advancing quantum computing and machine learning efficiency.

The architecture consists of two graph convolutional layers with dimensions (64,5) specified by the network’s design, followed by a CNN layer that aggregates node features into a single embedding ...

However, a relatively new form of quantile regression is neural network quantile regression -- a variation of neural network regression. By using a custom loss function that penalizes low predictions ...

In machine-learning-assisted high-throughput defect studies, a defect-aware latent representation of the supercell structure is crucial for the accurate prediction of defect properties. The ...

A machine learning-based drug screening technique has been developed and optimized using a novel, stitched neural network architecture with trainable, graph convolution-based fingerprints as a base ...

Revolutionizing fragrance design using deep neural networks (DNNs) scent profiles from chemical data The study demonstrates how DNNs predict fragrance profiles from essential oil chemical ...

Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional ...