Schad referred to his experience building machine learning ... down a lot more computation down to the servers. SatelliteGraphs goes in a similar direction: Replicating graphs to each cluster ...

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 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 ...

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 ...

Created by the Google Brain team and initially released to the public in 2015, TensorFlow is an open source library for numerical computation and large-scale machine learning. TensorFlow bundles ...

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 ...

For Professor Veljko Milutinovic of the University of Belgrade, computing stands on the edge of major change and it is one that was predicted by physicist Richard Feynman because of the way computing ...

Bringing knowledge graph and machine learning technology together can improve the accuracy of the outcomes and augment the potential of machine learning approaches. With knowledge graphs, AI language ...

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 ...