From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...

A California-based company is set to deliver more than 600 machine-learning infused strike drones ...

A study led by UC Riverside researchers offers a practical fix to one of artificial intelligence's toughest challenges by ...

AI is the broad goal of creating intelligent systems, no matter what technique is used. In comparison, Machine Learning is a specific technique to train intelligent systems by teaching models to learn ...

Abstract: Federated Learning (FL) has emerged as a cutting-edge paradigm in machine learning, showcasing remarkable advancements in recent years. This research paper delves into the dynamic landscape ...

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...

ABSTRACT: Machine learning (ML) has revolutionized risk management by enabling organizations to make data-driven decisions with higher accuracy and speed. However, as machine learning models grow more ...

This project was developed as part of my Master's programm at Heilbronn University. The goal is to classify different oil samples (e.g. olive oil, sunflower oil) based on their fluorescence and ...

This Research Topic explores the application of Machine Learning (ML) and Deep Learning (DL) methods in Neuromarketing and Consumer Neuroscience. Rather than following the prevailing “AI trend” ...

Artificial intelligence has become a buzzword in today's world, with nearly every smartphone launched in the previous two years basing its marketing around AI in some shape or form. It's gotten to a ...