OpenAI researchers are experimenting with a new approach to designing neural networks, with the aim of making AI models easier to understand, debug, and govern. Sparse models can provide enterprises ...

Recent advancements in neural network optimisation have significantly improved the efficiency and reliability of these models in handling complex tasks ranging from pattern recognition to multi-class ...

A new technical paper titled “Solving sparse finite element problems on neuromorphic hardware” was published by researchers ...

Researchers have employed Bayesian neural network approaches to evaluate the distributions of independent and cumulative ...

Nvidia’s latest pitch for the future of graphics is not about more polygons or higher memory bandwidth, it is about teaching ...

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released learnable quantum spectral filter technology for hybrid graph neural networks. This ...

(A–C) Representative images reconstructed by conventional method (left) and new method (right) of microtubules, nuclear pore complexes and F-actin samples. The regions enclosed by the white boxes are ...

A new technical paper titled “Hardware Acceleration for Neural Networks: A Comprehensive Survey” was published by researchers ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...

The representation of individual memories in a recurrent neural network can be efficiently differentiated using chaotic recurrent dynamics.