By tapping into a decades-old mathematical principle, researchers are hoping that Kolmogorov-Arnold networks will facilitate scientific discovery.

This book explores the interdisciplinary project that brings the long tradition of humanistic inquiry in architecture together with cutting-edge research in artificial intelligence. The main goal ...

So how is a designer supposed to deal with what are literally thousands of options for the network architecture? Here are some guidelines for novice neural network engineers.

Neural architecture search is a series of machine learning techniques that can help discover optimal neural networks for a given problem.

A new area in artificial intelligence involves using algorithms to automatically design machine-learning systems known as neural networks, which are more accurate and efficient than those ...

This perceptron design endows the quantum neural network with powerful expressive capabilities, enabling it to adapt to complex quantum data patterns while reducing computational errors.

In this work, we discuss these challenges and present our exploration of optimizing event-based neural network inference on SENECA, a scalable and flexible neuromorphic architecture. To address the ...

To address these limitations, this work proposes a novel neural architecture and hardware accelerator co-design framework, called CODEBench. It is composed of two new benchmarking sub-frameworks, ...

This book explores the interdisciplinary project that brings the long tradition of humanistic inquiry in architecture together with cutting-edge research in artificial intelligence.