44 citations · 94 across the 18 of their papers we have counts for
7 papers · 1 filter
Delta-Closure Structure for Studying Data Distribution
Aleksey Buzmakov, Tatiana Makhalova, Sergei O. Kuznetsov +1
In this paper, we revisit pattern mining and study the distribution underlying a binary dataset thanks to the closure structure which is based on passkeys, i.e., minimum generators…
Reducing Unintended Bias of ML Models on Tabular and Textual Data
Guilherme Alves, Maxime Amblard, Fabien Bernier +2
Unintended biases in machine learning (ML) models are among the major concerns that must be addressed to maintain public trust in ML. In this paper, we address process fairness of…
A Bayesian Convolutional Neural Network for Robust Galaxy Ellipticity Regression
Claire Theobald, Bastien Arcelin, Frédéric Pennerath +3
Cosmic shear estimation is an essential scientific goal for large galaxy surveys. It refers to the coherent distortion of distant galaxy images due to weak gravitational lensing al…
A Bayesian Neural Network based on Dropout Regulation
Claire Theobald, Frédéric Pennerath, Brieuc Conan-Guez +2
Bayesian Neural Networks (BNN) have recently emerged in the Deep Learning world for dealing with uncertainty estimation in classification tasks, and are used in many application do…
Discovering alignment relations with Graph Convolutional Networks: a biomedical case study
Pierre Monnin, Chedy Raïssi, Amedeo Napoli +1
Knowledge graphs are freely aggregated, published, and edited in the Web of data, and thus may overlap. Hence, a key task resides in aligning (or matching) their content. This task…
Making ML models fairer through explanations: the case of LimeOut
Guilherme Alves, Vaishnavi Bhargava, Miguel Couceiro +1
Algorithmic decisions are now being used on a daily basis, and based on Machine Learning (ML) processes that may be complex and biased. This raises several concerns given the criti…