Rakuten Group, Inc.

Dinesh Daultani

Mitsuru Nakazawa

Bruno Andre Charron

An information processing apparatus includes an acquiring unit configured to acquire an object image that includes an object, and a predicting unit configured to predict a class of the object by applying the object image acquired by the acquiring unit to a learning model. The learning model includes a main network () that extracts feature amounts regarding hierarchical classes of the object based on the object image, and a sub network () that outputs hierarchical classes of the object based on the feature amounts. The main network () includes extractors that extract the feature amounts. The sub network () includes classifiers that output classes of the object based on the feature amounts, and in the sub network, a higher-level classifier has a connection to one or more lower-level classifiers.

Information processing apparatus, information processing method, non-transitory computer readable medium, and learning model

An information processing apparatus includes: a generating unit configured to generate, from a data group including training data elements given different correct labels, multiple training data sets in such a manner that the number of included training data elements changes successively, by successively selecting one or more training data elements from the data group; and a training unit configured to repeatedly train a learning model for machine learning by applying the training data sets to the learning model.

Information processing apparatus, information processing method, and non-transitory computer readable medium

An input data generation unit is configured to generate, based on log data indicating a log of a behavior of a user with respect to a given computer resource for each period, input data for the period, which is associated with the log data. A user probability data generation unit is configured to generate user probability data based on output obtained from a trained machine learning model when the input data is input to the trained machine learning model. An anomaly determination unit is configured to determine whether an anomaly has occurred in the behavior of the user during a latest period based on the user probability data generated based on the input data for the latest period and the user probability data generated based on the input data for a period before the latest period.

Anomaly determining system, anomaly determining method and program

An information processing apparatus having a first fitness calculation to calculate a fitness using a predetermined function for models in a population; a virtual model generating to select, as parent models, models having higher value of the fitness using the first fitness calculation among the models, and generate a virtual model that outputs by performing calculation of output results of the selected parent models; a second fitness calculation to calculate the fitness of the virtual model using the predetermined function; a replacing operation constituting the population by adding the virtual model and by deleting a model having lower value of the fitness among the models in the population; and a model extracting to extract a model having higher value of the fitness from the population by repeating processing by the virtual model generating, the second fitness calculation, and the model replacing until a predetermined termination condition is reached.

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Tokyo, Japan

Dinesh Daultani