The data science field is evolving rapidly, driven by technological advancements, industry demands, and the growing ...

Such a learning paradigm often makes it more difficult to achieve the convergence of training models, and therefore the resulting network models are not easy to generalize, typically in machine ...

Transformer-based artificial neural networks have shown impressive results in natural ... validated and tested on two real-world datasets comprising various driving scenarios and battery conditions.

The STAR framework from Liquid AI uses evolutionary algorithms and a numerical encoding system to balance quality and efficiency in AI models.

This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you’re working with traditional statistical methods or cutting-edge deep learning ...

The range is from ongoing updates and improvements to a point-in-time release for thought leadership. Multinode Training Supported on a pyxis/enroot Slurm cluster. Deep Learning Compiler (DLC) ...

Many definitions of artificial intelligence include a comparison to the human mind or brain, whether in form or function.

Neural Architecture Search (NAS) is revolutionizing AI by automating the complex design of neural networks, a task ...

This is the model repository of YNU's deep learning principles and platform course assignments, which mainly use remote sensing image datasets to achieve classification, colorization and ...

This deep learning-based method utilizes an RNA language model to accurately predict RNA ... Central to RhoFold+ is the RNA ...