The paper, "Relational inductive biases, deep learning, and graph networks," posted on the arXiv pre-print service, is authored by Peter W. Battaglia of Google's DeepMind unit, along with ...

Reactive diagrams allow for a type of communication not possible in static mediums. Hover over this diagram to see how a neural turing machine shifts its attention over its old memory values to create ...

Neuromorphic NPU Sips Power to Handle Edge Machine-Learning Models Oct. 17, 2024 BrainChip’s Akida Pico neural processing unit, which leverages spiking neural networks, targets low-power IoT and ...

The A11 Bionic features a neural engine that the company says is "purpose-built for machine-learning," among other things. Tech's biggest players have fully embraced the AI revolution.

LAS VEGAS, Jan. 06, 2020 (GLOBE NEWSWIRE) -- (CES 2020) – NXP Semiconductors N.V. (NASDAQ: NXPI) today expanded its industry-leading EdgeVerse portfolio with the i.MX 8M Plus application ...

In addition to pure deep neural networks (DNNs), sometimes people use hybrid vision models, which combine deep learning with classical machine learning algorithms that perform specific sub-tasks.

Machine learning is proving to be invaluable in areas such as marketing, health care and autonomous cars. Neural networks and deep learning. If machine learning is an aspect of artificial intelligence ...

In this collection we highlight a selection of recent computational studies published in Nature Communications, featuring advances in computational chemistry methods and progress towards machine ...