A team of researchers in the Netherlands has proposed a new way of designing computer models of the brain—an approach that ...

Identification of each animal in a collective becomes possible even when individuals are never all visible simultaneously, enabling faster and more accurate analysis of collective behavior.

Analogue engineering still relies heavily on manual intervention, but that is changing with the growing use of AI/ML.

Abstract: This paper presents attention-based deep neural networks for high-dimensional microwave modeling to predict behavior of spatio-temporal modulated (STM) non-reciprocal bandpass filters ...

Draft version. Final version is published in “Chapman & Hall/CRC Mathe-matics and Artificial Intelligence Series” by Taylor & Francis in 2026. @book{ye2026mathematical, title = {Mathematical ...

Massive computing systems are required to train neural networks. The prodigious amount of consumed energy makes the creation of AI applications significant polluters ...

The package contains a mixture of classic decoding methods and modern machine learning methods. For regression, we currently include: Wiener Filter, Wiener Cascade, Kalman Filter, Naive Bayes, Support ...

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

Abstract: This work investigates the generalization behavior of deep neural networks (DNNs), focusing on the phenomenon of “fooling examples,” where DNNs confidently classify inputs that appear random ...

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...