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ARTICLE 06.01.2025
Unlocking the power of unsupervised learning with interpretable graph embeddings

Unlocking the power of unsupervised learning with interpretable graph embeddings

Unsupervised learning offers immense potential, but deciphering the results is often a challenge. Discover INGENIOUS, a groundbreaking framework from Euranova that generates interpretable graph embeddings that reveal the 'why' behind complex data patterns, empowering organizations to make informed decisions with confidence.

RESEARCH PAPER 04.08.2025

Muppet: A Modular and Constructive Decomposition for Perturbation-based Explanation Methods

The topic of explainable AI has recently received attention driven by a growing awareness of the need for transparent and accountable AI. In this paper, we propose a novel methodology to decompose any state-of-the-art perturbation-based explainability approach into four blocks. In addition, we provide Muppet: an open-source Python library for explainable AI.

RESEARCH PAPER 10.09.2025

Investigating a Feature Unlearning Bias Mitigation Technique for Cancer-type Bias in AutoPet Dataset

We proposed a feature unlearning technique to reduce cancer-type bias, which improved segmentation accuracy while promoting fairness across sub-groups, even with limited data.