pylfit

pylfit

Tony Ribeiro (1, 2, 3) , Maxime Folschette (4) , Morgan Magnin (1, 2) , Katsumi Inoue (2) , Chiaki Sakama (5) , Olivier Roux (1) , Sophie Tourret (6, 7) , Madeleine Eyraud (4)

1 LS2N - équipe MéForBio - Méthodes Formelles pour la Bioinformatique
2 NII - National Institute of Informatics
3 Chercheur indépendant
4 BioComputing
5 Wakayama University
6 MPII - Max-Planck-Institut für Informatik
7 VERIDIS - Modeling and Verification of Distributed Algorithms and Systems

Python implementation of the main algorithms of the Learning From Interpretation Transitions (LFIT) framework. - GULA: General Usage LFIT Algorithm - PRIDE: Polynomial Relational Inference of Dynamic Environnement - Synchronizer

Implémentation Python des algorithmes principaux de l'approche Learning From Interpretation Transitions (LFIT).

Mots clés: Learning From Interpretation Transitions; Symbolic machine learning; Explainable AI; Biological regulation networks; Inductive logic programming

Domaines: Machine Learning [stat.ML]; Intelligence artificielle [cs.AI]; Logique [math.LO]; Bio-Informatique, Biologie Systémique [q-bio.QM]

Tony Ribeiro, Maxime Folschette, Morgan Magnin, Katsumi Inoue, Chiaki Sakama, et al.. pylfit. 2022, ⟨swh:1:dir:253fc595a7b0695e8b58396de8a0dbecfcc2d4f8;origin=https://hal.archives-ouvertes.fr/hal-04435180;visit=swh:1:snp:32e97fa297205b15881d7e629637ebaf1167a1f7;anchor=swh:1:rel:bd05bc079cbaec08139ca176051e66b656cd64ee;path=/⟩. ⟨hal-04435180⟩