Seguir
Adrien Bibal
Adrien Bibal
Principal Scientist, InferLink
Dirección de correo verificada de cuanschutz.edu
Título
Citado por
Citado por
Año
Interpretability of machine learning models and representations: an introduction.
A Bibal, B Frénay
ESANN, 77-82, 2016
1522016
Legal requirements on explainability in machine learning
A Bibal, M Lognoul, A de Streel, B Frénay
Artificial Intelligence and Law 29, 149–169, 2020
1432020
Is Attention Explanation? An Introduction to the Debate
A Bibal, R Cardon, D Alfter, R Souza Wilkens, X Wang, T François, ...
ACL, 3889-3900, 2022
472022
Recasting a Traditional Course into a MOOC by Means of a SPOC
S Combéfis, A Bibal, P Van Roy
European MOOCs Stakeholders Summit, 205-208, 2014
452014
Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency
A Barragán-Montero, A Bibal, MH Dastarac, C Draguet, G Valdes, ...
Physics in Medicine & Biology 67 (11), 11TR01, 2022
302022
Explaining t-SNE Embeddings Locally by Adapting LIME
A Bibal, VM Vu, G Nanfack, B Frénay
ESANN, 393-398, 2020
172020
Achieving Rotational Invariance with Bessel-Convolutional Neural Networks
V Delchevalerie, A Bibal, B Frénay, A Mayer
NeurIPS 34, 28772-28783, 2021
152021
BIR: A method for selecting the best interpretable multidimensional scaling rotation using external variables
R Marion, A Bibal, B Frénay
Neurocomputing 342, 83-96, 2019
152019
ML + FV = ? A Survey on the Application of Machine Learning to Formal Verification
M Amrani, L Lúcio, A Bibal
arXiv preprint arXiv:1806.03600, 2018
142018
Measuring Quality and Interpretability of Dimensionality Reduction Visualizations
A Bibal, B Frénay
ICLR Workshop on SafeML, 2019
112019
Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features
A Bibal, R Marion, B Frénay
ESANN, 537-542, 2018
112018
Impact of Legal Requirements on Explainability in Machine Learning
A Bibal, M Lognoul, A de Streel, B Frénay
ICML Workshop on Law & Machine Learning, 2020
92020
IXVC: An interactive pipeline for explaining visual clusters in dimensionality reduction visualizations with decision trees
A Bibal, A Clarinval, B Dumas, B Frénay
Array 11, 100080, 2021
82021
Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment
A Bibal, B Frénay
NIPS Workshop on Interpretable Machine Learning in Complex Systems, 2016
72016
DT-SNE: T-SNE discrete visualizations as decision tree structures
A Bibal, V Delchevalerie, B Frénay
Neurocomputing 529, 101-112, 2023
62023
Linguistic Corpus Annotation for Automatic Text Simplification Evaluation
R Cardon, A Bibal, R Souza Wilkens, D Alfter, M Norré, A Müller, P Watrin, ...
EMNLP, 1842-1866, 2022
62022
User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning
A Bibal, B Dumas, B Frénay
EGC Workshop on Advances in Interpretable Machine Learning and Artificial …, 2019
62019
BIOT: Explaining multidimensional nonlinear MDS embeddings using the Best Interpretable Orthogonal Transformation
A Bibal, R Marion, R von Sachs, B Frénay
Neurocomputing 453, 109-118, 2021
52021
HCt-SNE: Hierarchical Constraints with t-SNE
VM Vu, A Bibal, B Frénay
International Joint Conference on Neural Networks (IJCNN), 2021
52021
Constraint Preserving Score for Automatic Hyperparameter Tuning of Dimensionality Reduction Methods for Visualization
MV Vu, A Bibal, B Frenay
IEEE Transactions on Artificial Intelligence 2 (3), 269-282, 2021
52021
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20