Machine learning (ML) is rapidly emerging as a powerful tool to improve the safety, reliability, and long-term performance of marine structures exposed to harsh ocean environments. This study presents ...

This repository contains various machine learning implementations and examples ranging from classic reinforcement learning (Q-Learning) to advanced deep learning techniques (CNN, LSTM, GAN, GNN). Each ...

However, NGD faces several challenges associated with gamma-ray generation and attenuation complexities. Unlike GGD, which utilizes 0.662 MeV monoenergetic γ rays from a 137 Cs source, NGD employs ...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...

Sometimes we assume the people and things around us are neutral or hostile to our existence. What if the opposite could be true? By Melissa Kirsch Normally I pass my morning commute absorbed in a book ...

Hyperparameter tuning is critical to the success of cross-device federated learning applications. Unfortunately, federated networks face issues of scale, heterogeneity, and privacy; addressing these ...

ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...

Start a Ray head node Connect and start Ray worker nodes via SSH Activate virtual environments and configure PYTHONPATH on all nodes 📌 Before running the script, ensure passwordless SSH access from ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now A new study by Anthropic shows that ...