Adrien Bibal

Orcid: 0000-0002-8650-8635

According to our database1, Adrien Bibal authored at least 30 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
RecSOI: recommending research directions using statements of ignorance.
J. Biomed. Semant., December, 2024

Granting GPT-4 License and Opportunity: Enhancing Accuracy and Confidence Estimation for Few-Shot Event Detection.
CoRR, 2024

2023
DT-SNE: t-SNE discrete visualizations as decision tree structures.
Neurocomputing, April, 2023

Predicting User Preferences of Dimensionality Reduction Embedding Quality.
IEEE Trans. Vis. Comput. Graph., 2023

SO(2) and O(2) Equivariance in Image Recognition with Bessel-Convolutional Neural Networks.
CoRR, 2023

Annotation Linguistique pour l'Évaluation de la Simplification Automatique de Textes.
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.
IEEE Trans. Artif. Intell., 2022

L'Attention est-elle de l'Explication ? Une Introduction au Débat (Is Attention Explanation ? An Introduction to the Debate ).
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.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

AIMLAI: Advances in Interpretable Machine Learning and Artificial Intelligence.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

Is Attention Explanation? An Introduction to the Debate.
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.
IEEE Trans. Artif. Intell., 2021

BIOT: Explaining multidimensional nonlinear MDS embeddings using the Best Interpretable Orthogonal Transformation.
Neurocomputing, 2021

DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality.
CoRR, 2021

IXVC: An interactive pipeline for explaining visual clusters in dimensionality reduction visualizations with decision trees.
Array, 2021

Legal requirements on explainability in machine learning.
Artif. Intell. Law, 2021

Achieving Rotational Invariance with Bessel-Convolutional Neural Networks.
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.
Proceedings of the Third International Workshop on Learning with Imbalanced Domains: Theory and Applications, 2021

HCt-SNE: Hierarchical Constraints with t-SNE.
Proceedings of the International Joint Conference on Neural Networks, 2021

iPMDS: Interactive Probabilistic Multidimensional Scaling.
Proceedings of the International Joint Conference on Neural Networks, 2021

Accelerating $t$-SNE using Fast Fourier Transforms and the Particle-Mesh Algorithm from Physics.
Proceedings of the International Joint Conference on Neural Networks, 2021

2020
Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction
PhD thesis, 2020

Impact of Legal Requirements on Explainability in Machine Learning.
CoRR, 2020

Explaining t-SNE Embeddings Locally by Adapting LIME.
Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

AIMLAI'20: Third Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence.
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.
Neurocomputing, 2019

2018
ML + FV = ♡? A Survey on the Application of Machine Learning to Formal Verification.
CoRR, 2018

Finding the most interpretable MDS rotation for sparse linear models based on external features.
Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

2016
Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment.
CoRR, 2016

Interpretability of machine learning models and representations: an introduction.
Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016


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