4 citations · 8 across the 5 of their papers we have counts for
5 papers
SO(2) and O(2) Equivariance in Image Recognition with Bessel-Convolutional Neural Networks
Valentin Delchevalerie, Alexandre Mayer, Adrien Bibal +1
For many years, it has been shown how much exploiting equivariances can be beneficial when solving image analysis tasks. For example, the superiority of convolutional neural networ…
DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality
Cristina Morariu, Adrien Bibal, Rene Cutura +2
A plethora of dimensionality reduction techniques have emerged over the past decades, leaving researchers and analysts with a wide variety of choices for reducing their data, all t…
Impact of Legal Requirements on Explainability in Machine Learning
Adrien Bibal, Michael Lognoul, Alexandre de Streel +1
The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes…
ML + FV = ? A Survey on the Application of Machine Learning to Formal Verification
Moussa Amrani, Levi Lúcio, Adrien Bibal
Formal Verification (FV) and Machine Learning (ML) can seem incompatible due to their opposite mathematical foundations and their use in real-life problems: FV mostly relies on dis…
Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment
Adrien Bibal, Benoit Frénay
In order to be useful, visualizations need to be interpretable. This paper uses a user-based approach to combine and assess quality measures in order to better model user preferenc…