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Deep learning regularization techniques to genomics data

Abstract : Deep Learning algorithms have achieved a great success in many domains where large scale datasets are used. However, training these algorithms on high dimensional data requires the adjustment of many parameters. Avoiding overfitting problem is difficult. Regularization techniques such as L 1 and L 2 are used to prevent the parameters of training model from being large. Another commonly used regularization method called Dropout randomly removes some hidden units during the training phase. In this work, we describe some architectures of Deep Learning algorithms, we explain optimization process for training them and attempt to establish a theoretical relationship between L 2-regularization and Dropout. We experimentally compare the effect of these techniques on the learning model using genomics datasets.
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Submitted on : Monday, January 31, 2022 - 9:47:16 AM
Last modification on : Friday, April 15, 2022 - 1:16:04 PM

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Harouna Soumare, Alia Benkahla, Nabil Gmati. Deep learning regularization techniques to genomics data. Array, Elsevier, 2021, 11, pp.100068. ⟨10.1016/j.array.2021.100068⟩. ⟨pasteur-03548830⟩

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