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 study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...

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 ...

Abstract: As an emerging machine learning task, high-dimensional hyperparameter optimization (HO) aims at enhancing traditional deep learning models by simultaneously optimizing the neural networks’ ...

As part of Valve's big hardware announcements today, the company did not disclose a price for the new Steam Machine device. Hardware engineer Yazan Aldehayyat was asked in an interview about what ...

Just how powerful will the Steam Machine be? Based on expert analysis of the specs revealed by Valve when it announced the Steam Machine on Wednesday, its new compact, console-like gaming PC is aimed ...

Valve revealed its next batch of at-home hardware on Wednesday, including a new take on the Steam Machine, a gaming PC that's intended to be used like a console. Also revealed were the Steam Frame, ...

Steam Machines are back for the first time since Valve teamed up with manufacturers like Alienware and Lenovo back in the 2010s. But while those original console-PC hybrids failed because of a lack of ...

Machine learning (ML) has rapidly become one of the most influential technologies across industries, from healthcare and finance to e-commerce and entertainment. But if you’re new to ML, the process ...

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 ...

ABSTRACT: This study presents a comprehensive and interpretable machine learning pipeline for predicting treatment resistance in psychiatric disorders using synthetically generated, multimodal data.