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Boundary-Based Fairness Constraints in Decision Trees and Random Forests

Decision Trees (DTs) and Random Forests (RFs) are popular models in Machine Learning (ML) thanks to their interpretability and …

Géraldin Nanfack, Valentin Delchevalerie, Benoît Frénay

Boundary-Based Fairness Constraints in Decision Trees and Random Forests

Global Explanations with Decision Rules: a Co-learning Approach

Black-box machine learning models can be extremely accurate. Yet, in critical applications such as in healthcare or justice, if models …

Géraldin Nanfack, Paul Temple, Benoît Frénay

Global Explanations with Decision Rules: a Co-learning Approach

Explaining t-SNE Embeddings Locally by Adapting LIME

Decision Trees (DTs) and Random Forests (RFs) are popular models in Machine Learning (ML) thanks to their interpretability and …

Adrien Bibal, Minh Viet Vu, Géraldin Nanfack, Benoît Frénay

Explaining t-SNE Embeddings Locally by Adapting LIME

Squeeze-SegNet: a new fast deep convolutional neural network for semantic segmentation

The recent researches in Deep Convolutional Neural Network have focused their attention on improving accuracy that provide significant …

Géraldin Nanfack, Azeddine Elhassouny, Rachid Oulad Haj Thami

Squeeze-SegNet: a new fast deep convolutional neural network for semantic segmentation

An example conference paper

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Géraldin Nanfack, Robert Ford

Jul 1, 2013

An example conference paper