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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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Blog Post number 1
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news
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publications
Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets
Published in NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications, 2022, 2022
An extension of mixup in the frequency domain that regularizes the deep nets for robustness to common corruptions.
Recommended citation: Ngnawe, J., NJIFON, M. A., Heek, J., and Dauphin, Y. Robustmix: Improving robustness by regularizing the frequency bias of deep nets. In NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications, 2022. URL https://openreview.net/forum?id=Na64z0YpOx.
Download Paper | Download Slides
Layerwise Early Stopping for Test Time Adaptation.
Published in Arxiv, 2024
A gradient alignement method for layer selection in Test Time Adaptation.
Recommended citation: Sahoo, S., ElAraby, M., Ngnawe, J., Pequignot, Y., Precioso, F., & Gagné, C. (2024). Layerwise Early Stopping for Test Time Adaptation. arXiv preprint arXiv:2404.03784.
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Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers.
Published in Neurips 2024, 2024
A novel property of deep robust classifiers that allows to use the logit margin as a proxy score for input margin and efficiently detect non-robust samples, vulnerable to adversarial attacks.
Recommended citation: Ngnawé, J., Sahoo, S., Pequignot, Y., Precioso, F., & Gagné, C. (2024). Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers. arXiv preprint arXiv:2406.18451.
Download Paper | Download Slides
talks
KmerAI Talks | Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers |
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teaching
Teaching Assistant (Tutor) at AIMS-Cameroon
Master's course, AIMS-Cameroon, 2017
Teaching assistant at the Mathematical Institute for Mathematical Sciences in Cameroon.
Crash Course Introduction to Statistical Machine Learning
Seminar, Ecole Nationale Supérieure Polytechnique de Yaoundé, Computer Engineering Department, 2019
A one week introductory class to statistical machine learning to 4th year students based on the lecture notes of Prof. Marc Deisenroth (AMMI2018)
Teaching Assistant/Introduction to Machine Learning by Christian Gagné
Undergraduate and Graduate course, Université Laval, 2022
Course description on Prof. Christian Gagné’s page Fall sessions: 2022, 2023, 2024 French: Introduction à l’apprentissage automatique GIF-7005 English: Introduction to Machine Learning GIF-7015