Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...

“Deep neural networks (DNNs) are typically trained using the conventional stochastic gradient descent (SGD) algorithm. However, SGD performs poorly when applied to train networks on non-ideal analog ...

Rice University computer scientists have overcome a major obstacle in the burgeoning artificial intelligence industry by showing it is possible to speed up deep learning technology without specialized ...

Often, when we think of getting a computer to complete a task, we contemplate creating complex algorithms that take in the relevant inputs and produce the desired behaviour. For some tasks, like ...

In the ever-evolving world of artificial intelligence, deep neural networks (DNNs) have revolutionized data processing, offering unparalleled accuracy across various ...

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. There’s a growing interest in employing autonomous mobile ...

Google LLC today detailed RigL, an algorithm developed by its researchers that makes artificial intelligence models more hardware-efficient by shrinking them. Neural networks are made up of so-called ...

The use of machine learning to perform blood cell counts for diagnosis of disease instead of expensive and often less accurate cell analyzer machines has nevertheless been very labor-intensive as it ...

The best way to understand neural networks is to build one for yourself. Let's get started with creating and training a neural network in Java. Artificial neural networks are a form of deep learning ...