Kabushiki Kaisha Toshiba

Toshiba Digital Solutions Corporation

Hidemasa Itou

Yukio Kamatani

Shinichi Kashimoto

Takashi Morimoto

Yuuji Irimoto

Shintarou Takahashi

Toshiyuki Katou

An information processing device of embodiments includes a data acquirer and a network processor. The data acquirer is configured to acquire graph structure data that includes a plurality of real nodes and one or more real edges connecting two of the plurality of real nodes. The network processor is configured to execute processing of propagating a feature amount of a k−1layer of each of a plurality of assumed nodes that include the plurality of real nodes and the one or more real edges at least to a feature amount of a klayer of another assumed node in a connection relationship with each of the assumed nodes in a neural network on the basis of the graph structure data acquired by the data acquirer. k is a natural number equal to or more than 1.

Information processing device, information processing method, and storage medium

A data processing apparatus according to an embodiment includes a data acquisition unit, a setting unit, and a reinforcement learning unit. The data acquisition unit acquires graph-structured data describing a connection relation between nodes. The setting unit sets a first network representing the graph-structured data acquired by the data acquisition unit. The reinforcement learning unit derives a parameter of the first network such that a feature quantity of an output layer of an evaluation target node in the first network approaches a reward and a feature quantity of an output layer of an operation node becomes a feature quantity causing the feature quantity of the output layer of the evaluation target node to approach the reward.

Data processing apparatus, data processing method, and storage medium

An abnormality detection device of an embodiment includes an encoder, a first identifier, a decoder, and a second identifier. The encoder is configured to compress input data using a compression parameter to generate a compressed data. The first identifier is configured to determine whether a distribution of the compressed data input by the encoder is a distribution of the compressed data or a prior distribution prepared in advance, and inputs a first identification result to the encoder. The decoder is configured to decode the compressed data using a compressing parameter to generate reconstructed data. The second identifier is configured to determine whether the reconstructed data input by the decoder is the reconstruction data or the input data and outputs a second identification result to the encoder and the decoder.

Abnormality detection device, learning device, abnormality detection method, and learning method

According to one embodiment, a learning device includes an acquirer, a generator, a first identifier, an extractor, a second identifier, and a learning-processor. The acquirer acquires real data. The generator generates pseudo data of the real data using a first neural network. The first identifier identifies whether input data is the real data or the pseudo data. The extractor extracts features of data from the input data. The second identifier identifies whether the features extracted by the extractor are features of the real data or features of the pseudo data. The learning-processor learns the first neural network such that the pseudo data and the real data are not discriminated on the basis of an identification result of each identifier.

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Inagi, Japan

Hidemasa Itou