Next, unlike conventional physics-informed neural networks that only utilize macroscopic physical information, we constrain the training of the neural network by using dynamic metabolic flux analysis ...

This article proposes an improved general zeroing neural network model to suppress noise and to enhance the real-time performance of solving TVQP problems. The proposed model allows nonconvex ...

FramePack is a next-frame (next-frame-section) prediction neural network structure that generates videos progressively. FramePack compresses input contexts to a constant length so that the generation ...

LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks Abstract: Designing channel codes under low-latency constraints is one of the most demanding requirements in 5G standards.

Cellular Neural Networks (CNN) [wikipedia] [paper] are a parallel computing paradigm that was first proposed in 1988. Cellular neural networks are similar to neural networks, with the difference that ...