We are super proud of our colleague Madalina who defended two weeks ago her PhD at the Université libre de Bruxelles.
She studied unsupervised analysis of RNA sequencing protocols data, and brilliantly succeeded.
You rock Madalina!
Congrats, Dr Ciortan!
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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.
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.