Myriam Bontonou

Orcid: 0000-0002-0010-5457

According to our database1, Myriam Bontonou authored at least 16 papers between 2019 and 2024.

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

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Explaining models relating objects and privacy.
CoRR, 2024

A Comparative Analysis of Gene Expression Profiling by Statistical and Machine Learning Approaches.
CoRR, 2024

2023
Studying Limits of Explainability by Integrated Gradients for Gene Expression Models.
CoRR, 2023

2021
Leveraging data structure to learn from few examples: applications to computer vision and neuroimaging. (Exploiter la structure des données pour apprendre à partir de quelques exemples : applications à la vision assistée par ordinateur et à la neuro-imagerie).
PhD thesis, 2021

Predicting the Generalization Ability of a Few-Shot Classifier.
Inf., 2021

Graphs as Tools to Improve Deep Learning Methods.
CoRR, 2021

Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classification.
CoRR, 2021

Few-Shot Decoding of Brain Activation Maps.
Proceedings of the 29th European Signal Processing Conference, 2021

Similarity between Base and Novel Classes: a Predictor of the Performance in Few-Shot Classification of Brain Activation Maps?
Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, 2021

2020
Ranking Deep Learning Generalization using Label Variation in Latent Geometry Graphs.
CoRR, 2020

Few-shot Learning for Decoding Brain Signals.
CoRR, 2020

Predicting the Accuracy of a Few-Shot Classifier.
CoRR, 2020

Deep Geometric Knowledge Distillation with Graphs.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019
Comparing linear structure-based and data-driven latent spatial representations for sequence prediction.
CoRR, 2019

A Unified Deep Learning Formalism For Processing Graph Signals.
CoRR, 2019

Introducing Graph Smoothness Loss for Training Deep Learning Architectures.
Proceedings of the IEEE Data Science Workshop, 2019


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