In this paper, we propose a novel algorithm to improve uRLLWSNs’ performance by applying machine learning techniques and genetic algorithms. Using the K -means clustering algorithm to construct a ...

An experimental study shows that already small-scale quantum computers can boost the performance of machine learning algorithms.

One of the current hot research topics is the combination of two of the most recent technological breakthroughs: machine learning and quantum computing. An experimental study shows that already ...

After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, researchers organized them into a 'periodic table of machine learning' that can help ...

In this paper, we propose a machine learning-based automated framework for algorithm selection and configuration for MPC applications. This framework aids the online implementation of MPC by selecting ...

The team designed a fully dynamic APSP algorithm in the MPC model with low round complexity that is faster than all the existing static parallel APSP algorithms.

In parallel, the application of machine learning algorithms in biomedical research has gained significant traction (11). Machine learning, and specifically its subfield of deep learning, has shown ...

Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests.

Linear attention-based models are gaining attention for their faster processing speed and comparable performance to Softmax transformers. However, large language models (LLMs), due to their large size ...

In the era of Big Data, the computational demands of machine learning (ML) algorithms have grown exponentially, necessitating the development of efficient parallel computing techniques. This research ...