Published in Arxiv, 2025
We use Random Matrix Theory and regularization to prune deep neural networks, achieving state-of-the-art results on Imagenet with VITs. We also show theorems justifying our approach. Read more
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Published in Arxiv, 2024
We present a general framework to combine existing models in a robust manner. Our method is flexible, places few assumptions on the models aggregated, and performs well in our experiments Read more
Recommended citation: Bourdais, T., & Owhadi, H. (2024). Model aggregation: minimizing empirical variance outperforms minimizing empirical error. arXiv [Cs.LG]. Retrieved from http://arxiv.org/abs/2409.17267 https://arxiv.org/abs/2409.17267
Published in PNAS, 2023
This paper introduces Computational Hypergraph Discovery (CHD), a novel method for uncovering unknown functional relationships between variables within datasets, And represent them using a hypergraph. Read more
Recommended citation: Bourdais, T., Batlle, P., Yang, X., Baptista, R., Rouquette, N., & Owhadi, H. (2024). Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots. Proceedings of the National Academy of Sciences, 121(32), e2403449121. https://doi.org/10.1073/pnas.2403449121 https://www.pnas.org/doi/10.1073/pnas.2403449121
Published in Digital Biomarkers, 2023
My first paper ! This paper describes the work we did at Doc.ai for ShareCare on Myasthenia gravis (MG) with UCB. We devellopped a Deep Learning model based on ResNet50 to predict a key symptom of MG via selfie. This would allow clinical trials to accept more patients as part of a study in order to scale it, as well as follow almost in real time the evolution of a patients symptoms using an app on their phone. Read more
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