Yahoo Japan Corporation

Yukihiro Tagami

Yusuke Tanaka

Nobuyuki Shimizu

Hayato Kobayashi

Shingo Hoshino

Yuma Miki

Toru Hotta

Heishiro Kanagawa

A training apparatus includes an acquiring unit that acquires a first model including an input layer to which input information is input; a plurality of intermediate layers that executes a calculation based on a feature of the input information that has been input; and an output layer that outputs output information that corresponds to output of the intermediate layer. The training apparatus includes a training unit that trains the first model such that, when predetermined input information is input to the first model, the first model outputs predetermined output information that corresponds to the predetermined input information and intermediate information output from a predetermined intermediate layer among the intermediate layers becomes close to feature information that corresponds to a feature of correspondence information that corresponds to the predetermined input information.

Cross-domain recommender systems using domain separation networks and autoencoders

An information processing device includes a communication unit that acquires first image data in which an observation value observed at a time t is used as a pixel value and a learning processing unit that generates second image data in which an observation value predicted to be observed at a time t+n after the time t is used as a pixel value from the first image data acquired by the acquiring unit based on a learning model obtained by machine learning using the first image data, in which the machine learning occurs based on a comparison of the first image data in which an observation value observed at a target time is used as a pixel value and the second image data in which an observation value predicted to be observed at the target time is used as a pixel value.

Information processing device, data structure, information processing method, and non-transitory computer readable storage medium

An information processing apparatus according to the present application includes a classification unit, a conversion unit, a label acquiring unit. The classification unit that classifies a feature vector by using a classification vector, the feature vector being converted from target data. The conversion unit that converts the feature vector into an embedding vector in accordance with a conversion rule according to classification performed by the classification unit. The label acquiring unit that acquires, as one or more labels to be provided to the target data, one or more labels acquired based on the embedding vector converted by the conversion unit.

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

An information retrieval device includes a degree-of-association information storage unit capable of storing an item(s) of degree-of-association information indicating a degree of association between each of an item(s) of first information and each of an item(s) of second information; an accepting unit that accepts a query including an item(s) of query information which is/are an item(s) of information used for retrieval of content; a query converter that obtains, by using an item(s) of first information corresponding to each of the item(s) of query information, and the item(s) of degree-of-association information, an item(s) of second information whose degree of association with the item(s) of first information is greater as a predetermined condition is better satisfied; and a retrieval unit that retrieves content by using the item(s) of second information obtained by the query converter. Accordingly, content necessary for a user can be retrieved.

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Tokyo, Japan

Yukihiro Tagami