Extinction de neurone

Domaine

Intelligence artificielle
Apprentissage automatique
Apprentissage profond
Coulombe

Définition

Méthode de régularisation de l'apprentissage d'un réseau profond par extinction de neurones (dropout). Rappelons que la régularisation consiste en l'ajout de contraintes pour réduire le surajustement (overfitting).

Français

<poll> Choisissez parmi ces termes proposés : extinction de neurone maille de réseau point mort trou de neurone trou de mémoire trou de réseau </poll>

Anglais

Dropout

Dropout is a regularization technique for Neural Networks that prevents overfitting. It prevents neurons from co-adapting by randomly setting a fraction of them to 0 at each training iteration. Dropout can be interpreted in various ways, such as randomly sampling from an exponential number of different networks. Dropout layers first gained popularity through their use in CNNs, but have since been applied to other layers, including input embeddings or recurrent networks.

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting
• Recurrent Neural Network Regularization