A new research paper titled "Algebraic Machine Learning: Learning as Computing an Algebraic Decomposition of a Task" by Fernando Martin-Maroto, Nabil Abderrahaman, David Mendez, and Gonzalo G. de Polavieja introduces a novel approach to machine learning based on Abstract Algebra. This framework offers an alternative to traditional methods rooted in statistics and optimization, providing a new perspective on the foundations of learning.
The AML Dashboard is a web-based tool that allows users, developers and researchers to interact with the AML framework.
This document provides detailed instructions on deploying an Edge Node on a ROSbot 2R from Husarion. The Edge Node will capture images using the Orbbec Astra camera and sends them to the Inference Node deployed on a laptop to perform TensorFlow inference on the given image. If the TensorFlow inference detects the presence of a person with a probability of 80% or higher, the robot will turn.
The AML Dashboard is a web-based tool that allows users, developers, and researchers to interact with the AML framework.
The AML Dashboard is a web-based tool that enables users to interact seamlessly with the AML framework. Designed for developers looking to implement and test their own AML models, it provides an intuitive interface and advanced tools for model evaluation, fine-tuning, and validation across various scenarios. With this dashboard, users can monitor model performance, make real-time adjustments, and optimize deployment in production environments.
The ALMA project leverages AML properties to develop a new generation of interactive, human-centered machine learning systems.
Plaza de la Encina 10-11, Núcleo 4, 2ª Pl.
28760 Tres cantos - Madrid (España)
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952091.
