The authors report a kernel-based machine learning model capable of reconstructing ... Here the authors propose a graph-based molecular generative model that outperforms previously proposed ...

With industries increasingly adopting machine learning, it seems likely that knowledge graph technology will also evolve hand-in-hand. As well as being a useful format for feeding training data to ...

The paper elaborates on a technique for using knowledge graphs with machine learning; specifically, a branch of machine learning called reinforcement learning. This is something that holds great ...

A new study in Small introduces OptiMate, a machine learning model that predicts optical properties and identifies ...

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 ...

We take the opportunity to unpack what this means, and how it's related to the future of graph databases, as well as revisit interesting developments in Neptune's support for machine learning and ...

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 ...

Apple has released MLX, “an array framework for machine learning on Apple silicon, brought to you by Apple machine learning research.” MLX is designed by machine learning researchers for ...

This research is expected to open up new frontiers in the realms of optical computing and machine learning. “In the burgeoning field of all-optical machine learning, nonlinear optical layers are ...

Machine learning-based neural network potentials often cannot describe long-range interactions. Here the authors present an approach for building neural network potentials that can describe the ...