Displaying 4 resources
White paper White paper

Holistic framework for AI in critical network infrastructures

This document establishes the main foundations of the AI4REALNET project, in particular, the following key outcomes: - The formal specification of domain-specific use cases (UCs), replicating real-world operating scenarios involving human operators t
Category
Action and Interaction Technologies, Real-time context aware decision making, Policy, Regulation, Certificates, and Standards, Best practices in deployment, Legal framework and regulation, Recommendations towards policy changes, Reasoning and decision-making Technologies, Hybrid Decision Making, Trustworthiness, Systems, methodologies, hardware, and tools, Support tools, System architectures
Target audience
ADR Experts and Associations, Individual Citizens/Members of the Society, Policy Makers, Private Sector, Public Sector, Researchers and Academic
Source
Adra-e
Software resources Software resources

Decentralized-gnn

A package for implementing and simulating decentralized Graph Neural Network algorithms for classification of peer-to-peer nodes. Developed code supports the publication p2pGNN: A Decentralized Graph Neural Network for Node Classification i
Category
System architectures, Technology methodologies and landscape
Target audience
ADR Experts and Associations, Researchers and Academic
Source
Adra-e
Software resources Software resources

CO2A – Contrastive Conditional domain Alignment

A novel unsupervised domain adaptation approach for action recognition from videos, inspired by recent literature on contrastive learning.
Category
Multi-modal interaction, Sensing of motion and mechanical properties
Target audience
ADR Experts and Associations, Researchers and Academic
Source
Adra-e
Software resources Software resources

PandA: Unsupervised learning of parts and appearances in the feature maps of GANs

We propose an architecture-agnostic approach that jointly discovers factors representing spatial parts and their appearances in an entirely unsupervised fashion.
Category
System architectures
Target audience
ADR Experts and Associations, Researchers and Academic
Source
Adra-e