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 operator
Article/Books/eBooks
Uncertainty-Based Learning of a Lightweight Model for Multimodal Emotion Recognition
In this paper, the authors propose a lightweight neural network architecture that extracts and performs the analysis of multimodal information using the same audio and visual networks across multiple temporal segments.
Video/Webinars
Towards functional safety management for AI-based critical systems
The webinar provides attendees with a comprehensive understanding of the challenges and opportunities associated with integrating AI into safety-critical systems.
Software resources
Decentralized-gnn
A package for implementing and simulating decentralized Graph Neural Network algorithms for classification of peer-to-peer nodes.
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.
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.