Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: According to one aspect of an 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.

Abstract: 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.

Abstract: According to one aspect of an embodiment a learning device includes an acquisition unit that acquires a plurality of pieces of input information of different classifications. The learning device includes a learning unit that learns a model as a model when the pieces of input information are inputted, outputs a plurality of pieces of output information corresponding to the respective pieces of input information. The model includes a plurality of encoding parts that generate pieces of characteristic information indicating characteristics of the pieces of input information from the pieces of input information. The model includes a synthesizing part that generates synthesized information obtained by synthesizing the pieces of characteristic information generated by the encoding parts. The model includes a plurality of decoding parts that generate pieces of output information of different classifications from the synthesized information generated by the synthesizing part.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A relevance index correction apparatus includes an attribute information obtainer, a relevance index generator, and a specific relevance index calculator. The attribute information obtainer obtains, in response to selection of a specific piece of advertisement information that is to be posted on a web page, attribute information related to the specific piece of advertisement information. The relevance index generator generates an apparent relevance index and a mean relevance index. The apparent relevance index indicates an apparent degree of relevance of the attribute information with respect to the specific piece of advertisement information. The mean relevance index indicates a mean degree of relevance of the attribute information with respect to all of a plurality of pieces of advertisement information.

Abstract: A profit index value generation system includes a profit index value calculating unit. The profit index value calculating unit calculates a profit index value related to a link destination web page in accordance with (i) a selection probability value that indicates a probability of selection of link information, the link information being posted on a link source web page displayed on a display screen of a terminal and being linked to the link destination web page on which advertisement information is posted, and (ii) an expected profit value that indicates an amount of profit which is expected to be generated as a result of the advertisement information being displayed on the display screen together with the link destination web page.