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Andry Rasoanaivo

2 accepted papers

2022

Learning meta-features for AutoML

ICLR 2022spotlight

This paper tackles the AutoML problem, aimed to automatically select an ML algorithm and its hyper-parameter configuration most appropriate to the dataset at hand. The proposed approach, MetaBu, learns new meta-features via an Optimal Transport procedure, aligning the manually designed \mf s with th…

2020

ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network

ICASSP 2020accepted

Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super-resolution that is able to produce photorealistic images. Despite the visual quality of these generated images, there is still room for improvement. In this fashion, the model is…

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