Open source deep learning ... tool for network modeling and performance. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture ...

Neural architecture search promises to speed up the process of finding neural network architectures that ... along with feature engineering, transfer learning, and hyperparameter optimization.

We described the hardware architecture in detail back in March ... software work necessarily—it is rather a function of taking a graph approach to the deep neural network problem. “Learning is ...

With the use of proper neural network architecture (number of layers, number of neurons, non-linear function, etc.) along with large enough data, a deep learning network can learn any mapping from ...

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 ...

“Intel’s DLA (deep learning ... network is extended with an additional transport kernel, and connect that new kernel to the Xbar without affecting existing kernels or instructions. In addition to the ...

“Abstract—Large-scale distributed deep learning training has enabled developments of more complex deep neural network models to learn from ... and the opportunity of algorithm-architecture co-design.

A research team from Kumamoto University has developed a promising deep learning model that ... within large and complex networks. However, conventional Graph Neural Networks (GNNs) often struggle ...