Molecular machine learning (ML) underpins critical workflows in drug discovery, material science, and catalyst optimization ...

If you plan to use machine learning for research, consider ChatGPT’s shortcomings and inquire about AI tools’ training data ...

As artificial intelligence (AI) and machine learning (ML) continue to advance ... It is favored for its dynamic computation graph, ease of use, and strong support for neural network training. PyTorch ...

Its dynamic computation graph helps developers build and modify ... Importantly, it requires knowledge of machine learning and deep learning concepts, making it unsuitable for those looking ...

Scientist Yi Nian is sharing his machine-learning expertise with the world in his latest co-authored publication, “Globally Interpretable Graph Learning via Distribution Matching.” SEATTLE ...

Abstract: The requirement for appropriate ways to measure the similarity between data objects is a common but vital task in various domains, such as data mining, machine ... computation learning on ...

By extending the fuzzy topological indices to polynomial forms, this research drastically reduces the calculation ... a machine learning technique, to generate polynomials for adjacency and degree ...

Designing state-of-the-art deep learning models is an incredibly complex challenge ... Instead of fully training each candidate architecture, NASGraph converts them into graph representations and uses ...

"We started to look at the challenges current systems experienced when scaling state-of-the-art machine learning techniques for graphs to really big datasets ... we ended up being completely ...