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ANOMALY DETECTION ON SEQUENTIAL LOG DATA USING A RESIDUAL NEURAL NETWORK

机译:基于残差神经网络的连续测井数据异常检测

摘要

A multilayer perceptron herein contains an already-trained combined sequence of residual blocks that contains a semantic sequence of residual blocks and a contextual sequence of residual blocks. The semantic sequence of residual blocks contains a semantic sequence of layers of an autoencoder. The contextual sequence of residual blocks contains a contextual sequence of layers of a recurrent neural network. Each residual block of the combined sequence of residual blocks is used based on a respective survival probability. By the autoencoder and based on the using each residual block of the semantic sequence, a previous entry of a log is semantically encoded. By the recurrent neural network and based on the using each residual block of the contextual sequence, a next entry of the log is predicted. In an embodiment during training, survival probabilities are hyperparameters that are learned and used to probabilistically skip residual blocks such that the multilayer perceptron has stochastic depth.
机译:本文中的多层感知器包含已训练的残余块的组合序列,其包含残余块的语义序列和残余块的上下文序列。剩余块的语义序列包含自动编码器层的语义序列。剩余块的上下文序列包含递归神经网络的层的上下文序列。基于各自的生存概率使用剩余块的组合序列的每个剩余块。通过自动编码器并基于使用语义序列的每个剩余块,对日志的前一个条目进行语义编码。通过递归神经网络,并基于使用上下文序列的每个剩余块,预测日志的下一个条目。在训练期间的一个实施例中,生存概率是学习并用于概率地跳过剩余块的超参数,使得多层感知器具有随机深度。

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