and modern deep learning. They explored flexible learning-based approaches which implement strong relational inductive biases to capitalize on explicitly structured representations and computations, ...

Open source deep learning neural networks are coming of age ... retrieve the results of discretionary data on any edge of the graph. This is extremely helpful for debugging complicated ...

While deep learning has been extremely popular and ... For these simple neural networks, you can graph a bunch of data and draw a line and say things on one side of the line are in one category ...

Rose Yu has drawn on the principles of fluid dynamics to improve deep learning systems that predict traffic, model the ...

Also: Google Brain, Microsoft plumb the mysteries of networks with AI The paper, "Relational inductive biases, deep learning, and graph networks," posted on the arXiv pre-print service ...

Sheelam’s solution deploys layered deep learning agents that can anticipate network congestion, dynamically reconfigure communication parameters, and prioritize traffic based on real-time demand. His ...