This repository hosts the code, data, and model weights of GPT-ST. Furthermore, it also includes the code for the baselines used in the paper. GPT-ST is a generative pre-training framework for ...

This paper explores the integration of Artificial Intelligence (AI) large language models to empower the Python programming course for junior undergraduate students in the electronic information ...

This repository contains the code used to develop HOPNet, part of the publication titled "Integrating physics and topology in neural networks for learning rigid body ...

TensorFlow is an open-source framework developed by Google scientists and engineers for numerical computing. TensorFlow.NET is a library that provides a .NET Standard binding for TensorFlow, allowing ...

Neuromorphic computing algorithms based on Spiking Neural Networks (SNNs) are evolving to be a disruptive technology driving machine learning research. The overarching goal of this work is to develop ...

The interplay between data symmetries and network architecture is key for efficient learning in neural networks. Convolutional neural networks perform well in image recognition by exploiting the ...

Despite over 300 y of effort, no solutions exist for predicting when a general planetary configuration will become unstable. We introduce a deep learning architecture to push forward this problem for ...

Your browser does not support the audio element. Well, for one, you’ll gain a deeper understanding of how all the pieces are put together. By comparing your code ...