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arXiv 1809.08404math.CO
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深度学习的组合设计

Combinatorial Designs for Deep Learning

Shoko Chisaki, Ryoh Fuji-Hara, Nobuko Miyamoto

摘要:

深度学习是一种使用多层神经网络的机器学习方法。一个多层神经网络可以看作是一个完全二部图的链。第一个partita的节点是输入层,最后一个是输出层。二部图的边作为权值,权值用矩阵表示。通过将权重矩阵与第(i-1)个partita的值相乘来计算第i个partita的值。使用大量的训练和教师数据,逐步估计权重参数。过度拟合(或过度学习)是指一个模型,模型的“训练数据”太好。然后,模型很难推广到不在训练集中的新数据。避免过拟合的最流行的方法称为dropout。Dropout在训练过程中将激活(节点)的随机样本删除为零。节点的随机样本会导致更不规则的丢失边缘频率。在实验设计领域也有类似的抽样概念。我们提出了一个组合设计的辍学节点从每个partita平衡的边缘的频率。我们在本文中分析和构造这样的设计。

英文摘要:

Deep learning is a machine learning methodology using multi-layer neural network. A multi-layer neural network can be regarded as a chain of complete bipartite graphs. The nodes of the first partita is the input layer and the last is the output layer. The edges of a bipartite graph function as weights which are represented as a matrix. The values of i-th partita are computed by multiplication of the weight matrix and values of (i-1)-th partita. Using mass training and teacher data, the weight parameters are estimated little by little. Overfitting (or Overlearning) refers to a model that models the `training data` too well. It then becomes difficult for the model to generalize to new data which were not in the training set. The most popular method to avoid overfitting is called dropout. Dropout deletes a random sample of activations (nodes) to zero during the training process. A random sample of nodes causes more irregular frequency of dropout edges. There is a similar sampling concept in the area of design of experiments. We propose a combinatorial design on dropout nodes from each partita which balances the frequency of edges. We analyze and construct such designs in this paper.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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