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Sébastien Marmin

2 accepted papers

2023

Input uncertainty propagation through trained neural networks

ICML 2023poster

When physical sensors are involved, such as image sensors, the uncertainty over the input data is often a major component of the output uncertainty of machine learning models. In this work, we address the problem of input uncertainty propagation through trained neural networks. We do not rely on a G…

Cited by 2SourcePDFScholar
2020

Kernel Computations from Large-Scale Random Features Obtained by Optical Processing Units

ICASSP 2020accepted

Approximating kernel functions with random features (RFs) has been a successful application of random projections for nonparametric estimation. However, performing random projections presents computational challenges for large-scale problems. Recently, a new optical hardware called Optical Processin…

Cited by 0SourceScholar