Adrien Bibal

Neurocomputing, April, 2023

Predicting User Preferences of Dimensionality Reduction Embedding Quality.

[BibT_eX]

[DOI]

IEEE Trans. Vis. Comput. Graph., 2023

SO(2) and O(2) Equivariance in Image Recognition with Bessel-Convolutional Neural Networks.

[BibT_eX]

[DOI]

Alexandre Mayer

CoRR, 2023

Annotation Linguistique pour l'Évaluation de la Simplification Automatique de Textes.

[BibT_eX]

[DOI]

Proceedings of the Actes de CORIA-TALN 2023. Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles, TALN 2023, 2023

2022

Integrating Constraints Into Dimensionality Reduction for Visualization: A Survey.

[BibT_eX]

[DOI]

IEEE Trans. Artif. Intell., 2022

L'Attention est-elle de l'Explication ? Une Introduction au Débat (Is Attention Explanation ? An Introduction to the Debate ).

[BibT_eX]

[DOI]

Proceedings of the Actes de la 29e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale, 2022

Linguistic Corpus Annotation for Automatic Text Simplification Evaluation.

[BibT_eX]

[DOI]

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

AIMLAI: Advances in Interpretable Machine Learning and Artificial Intelligence.

[BibT_eX]

[DOI]

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

Is Attention Explanation? An Introduction to the Debate.

[BibT_eX]

[DOI]

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

Constraint Preserving Score for Automatic Hyperparameter Tuning of Dimensionality Reduction Methods for Visualization.

[BibT_eX]

[DOI]

IEEE Trans. Artif. Intell., 2021

BIOT: Explaining multidimensional nonlinear MDS embeddings using the Best Interpretable Orthogonal Transformation.

[BibT_eX]

[DOI]

Neurocomputing, 2021

DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality.

[BibT_eX]

[DOI]

CoRR, 2021

IXVC: An interactive pipeline for explaining visual clusters in dimensionality reduction visualizations with decision trees.

[BibT_eX]

[DOI]

Array, 2021

Legal requirements on explainability in machine learning.

[BibT_eX]

[DOI]

Artif. Intell. Law, 2021

Achieving Rotational Invariance with Bessel-Convolutional Neural Networks.

[BibT_eX]

[DOI]

Alexandre Mayer

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

GanoDIP - GAN Anomaly Detection through Intermediate Patches: a PCBA Manufacturing Case.

[BibT_eX]

[DOI]

Proceedings of the Third International Workshop on Learning with Imbalanced Domains: Theory and Applications, 2021

HCt-SNE: Hierarchical Constraints with t-SNE.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2021

iPMDS: Interactive Probabilistic Multidimensional Scaling.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2021

Accelerating $t$-SNE using Fast Fourier Transforms and the Particle-Mesh Algorithm from Physics.

[BibT_eX]

[DOI]

Alexandre Mayer

Proceedings of the International Joint Conference on Neural Networks, 2021

2020

Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction

[BibT_eX]

[DOI]

PhD thesis, 2020

Impact of Legal Requirements on Explainability in Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2020

Explaining t-SNE Embeddings Locally by Adapting LIME.

[BibT_eX]

[DOI]

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

AIMLAI'20: Third Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence.

[BibT_eX]

[DOI]

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

2019

BIR: A method for selecting the best interpretable multidimensional scaling rotation using external variables.

[BibT_eX]

[DOI]

Rebecca Marion

Neurocomputing, 2019

2018

ML + FV = ♡? A Survey on the Application of Machine Learning to Formal Verification.

[BibT_eX]

[DOI]

Moussa Amrani

Levi Lúcio

CoRR, 2018

Finding the most interpretable MDS rotation for sparse linear models based on external features.

[BibT_eX]

[DOI]

Rebecca Marion

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

2016

Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment.

[BibT_eX]

[DOI]

CoRR, 2016

Interpretability of machine learning models and representations: an introduction.

[BibT_eX]

[DOI]