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 ...

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 ...

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 updates expand the role for its tools, as TigerGraph’s product is becoming more of a data analytics and AI platform than just a mechanism for storing graph data. “Our biggest enterprises ...

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 ...

Molecular machine learning (ML) underpins critical workflows in drug discovery, material science, and catalyst optimization by rapidly predicting molecular interactions and properties. For instance, ...

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 ...

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 ...

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 ...

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 ...