It shows the schematic of the physics-informed neural network algorithm for pricing European options under the Heston model. ...

Neural network optimisation has emerged as a transformative approach in microwave engineering, driving enhancements in both the accuracy and speed of electromagnetic (EM) simulations and circuit ...

Machine learning techniques that make use of tensor networks could manipulate data more efficiently and help open the black ...

Neural network pruning is a key technique for deploying artificial intelligence (AI) models based on deep neural networks (DNNs) on resource-constrained platforms, such as mobile devices. However, ...

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, innovatively launches a quantum-enhanced deep convolutional neural network image 3D reconstruction ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, structureless data. Yet when trained on datasets with structure, they learn the ...

Even networks long considered "untrainable" can learn effectively with a bit of a helping hand. Researchers at MIT's Computer ...

In this architecture, the training process adopts a joint optimization mechanism based on classical cross-entropy loss. WiMi treats the measurement probability distribution output by the quantum ...

Red Hat, the IBM-owned open source software firm, is acquiring Neural Magic, a startup that optimizes AI models to run faster on commodity processors and GPUs. The terms of the deal weren’t disclosed.

Modern networks have outgrown reactive planning. With 5G and rising traffic, manual analysis and legacy models can’t keep up ...