Следене
Adrien Bibal
Adrien Bibal
BAEF Postdoctoral Fellow, University of Colorado Anschutz Medical Campus
Потвърден имейл адрес: cuanschutz.edu
Заглавие
Позовавания
Позовавания
Година
Interpretability of machine learning models and representations: an introduction.
A Bibal, B Frénay
ESANN, 77-82, 2016
1212016
Legal requirements on explainability in machine learning
A Bibal, M Lognoul, A de Streel, B Frénay
Artificial Intelligence and Law 29, 149–169, 2020
742020
Recasting a Traditional Course into a MOOC by Means of a SPOC
S Combéfis, A Bibal, P Van Roy
Proceedings of the European MOOCs Stakeholders Summit, 205-208, 2014
432014
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
162019
Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features
A Bibal, R Marion, B Frénay
ESANN, 537-542, 2018
132018
Measuring Quality and Interpretability of Dimensionality Reduction Visualizations
A Bibal, B Frénay
ICLR Workshop on SafeML, 2019
112019
Explaining t-SNE Embeddings Locally by Adapting LIME
A Bibal, VM Vu, G Nanfack, B Frénay
ESANN, 393-398, 2020
92020
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
92018
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
82020
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
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
62022
Achieving Rotational Invariance with Bessel-Convolutional Neural Networks
V Delchevalerie, A Bibal, B Frénay, A Mayer
NeurIPS 34, 28772-28783, 2021
42021
Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency
AM Barragan-Montero, A Bibal, M Huet, C Draguet, G Valdes, D Nguyen, ...
Physics in Medicine & Biology, 2022
32022
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
3*2021
HCt-SNE: Hierarchical Constraints with t-SNE
VM Vu, A Bibal, B Frénay
International Joint Conference on Neural Networks (IJCNN), 2021
32021
iPMDS: Interactive Probabilistic Multidimensional Scaling
VM Vu, A Bibal, B Frénay
International Joint Conference on Neural Networks (IJCNN), 2021
32021
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
32021
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
32019
Explaining the Black Box: when Law Controls AI
A de Streel, A Bibal, B Frénay, M Lognoul
CERRE, 2020
22020
Integrating Constraints into Dimensionality Reduction for Visualization: a Survey
VM Vu, A Bibal, B Frénay
IEEE Transactions on Artificial Intelligence, 2022
12022
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