Abstract: Nonintrusive load monitoring (NILM) has emerged as a pivotal technology in energy management, garnering significant attention in both research and engineering communities. Despite its ...

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 ...

For the first time, researchers at the Netherlands Institute for Neuroscience and Amsterdam UMC have identified what happens in neural networks deep within the brain during obsessive thoughts and ...

The series is designed as an accessible introduction for individuals with minimal programming background who wish to develop practical skills in implementing neural networks from first principles and ...

Despite the widespread success of neural networks, their susceptibility to adversarial examples remains a significant challenge. Adversarial training (AT) has emerged as an effective approach to ...

The series is designed as an accessible introduction for individuals with minimal programming background who wish to develop practical skills in implementing neural networks from first principles and ...

This is an application-oriented course with programming assignments in Python/Tensorflow. The course proceeds in three phases: Lecture part, project part, and excursion/project presentation. The ...

Introduction: Understanding human actions in complex environments is crucial for advancing applications in areas such as surveillance, robotics, and autonomous systems. Identifying actions from ...

This project presents the design and implementation of a Low-Power BFloat16 Pipelined MAC (Multiply-Accumulate) Unit for deep neural network (DNN) applications. The MAC unit is optimized for low power ...