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ARTIFICIAL NEURAL NETWORK AND METHOD OF TRAINING AN ARTIFICIAL NEURAL NETWORK WITH EPIGENETIC NEUROGENESIS

机译:具有表观神经发生的人工神经网络的人工神经网络与培养人工神经网络的方法

摘要

A method for retraining an artificial neural network trained on data from an old task includes training the artificial neural network on data from a new task different than the old task, calculating a drift, utilizing Sliced Wasserstein Distance, in activation distributions of a series of hidden layer nodes during the training of the artificial neural network with the new task, calculating a number of additional nodes to add to at least one hidden layer based on the drift in the activation distributions, resetting connection weights between input layer nodes, hidden layer nodes, and output layer nodes to values before the training of the artificial neural network on the data from the new task, adding the additional nodes to the at least one hidden layer, and training the artificial neural network on data from the new task.
机译:一种培训从旧任务的数据培训的人工神经网络的方法包括从旧任务的新任务中训练人工神经网络,从而计算漂移,利用切片的wasserstein距离,在一系列隐藏的激活分布中 在训练期间的层节点与新任务,计算许多附加节点以基于激活分布的漂移添加到至少一个隐藏层,重置输入层节点之间的连接权重,隐藏的层节点, 并输出层节点以在新任务中训练人工神经网络之前的值,将附加节点添加到至少一个隐藏层,并从新任务中训练人工神经网络上的数据。

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