Abstract: An information processing method includes obtaining a first inference result by a first inference model, obtaining a second inference result by a second inference model, and training the second inference model by machine learning to reduce an error calculated from the first inference result and the second inference result. The second inference model includes (a) a first coefficient pertaining to a domain and used for inference by the second inference model and (b) a second coefficient used for inference by the second inference model. The training includes; when it is determined that a predetermined condition is not satisfied, training the second inference model with the first coefficient and the second coefficient designated as targets for an update; and when it is determined that the predetermined condition is satisfied, training the second inference model with the second coefficient designated as a target for an update.

Abstract: Provided is an information processing method that includes: performing a conversion process on the first feature information to obtain a first conversion result; performing the conversion process on the second feature information to obtain a second conversion result; performing a projection process on the first conversion result to obtain a first projection result; performing the projection process on the second conversion result to obtain a second projection result; calculating an error between the first projection result and the second projection result; and training the second model to reduce the error. The conversion process produces an error between the first projection result and the second projection result that is greater than the error between a projection result obtained by performing the projection process on the first feature information and a projection result obtained by performing the projection process on the second feature information.

Abstract: An information processing method includes acquiring first output data for input data of first learning model, reference data for the input data, and second output data for the input data of second learning model obtained by converting first learning model; calculating first difference data corresponding to a difference between the first difference data and the reference data and second difference data corresponding to a difference between the second output data and the reference data; and training first learning model with use of the first difference data and the second difference data.

Abstract: In a data processing method executed by a computer: inputting, in a third trained model, first output data corresponding to first input data for a first trained model to obtain second output data, the third trained model being acquired through training in which (i) output data of the first trained model is used as training data, and (ii) output data of a second trained model acquired by converting the first trained model is used as label data; obtaining first label data of the first input data; and retraining the first trained model using first differential data corresponding to differences between the second output data and the first label data.

Abstract: An information processing method includes: obtaining a first inference model serving as a reference; computing a second inference model that is larger than the first inference model in model size, based on the first inference model; quantizing the second inference model computed to generate a third inference model; training the third inference model, using machine learning; determining whether a performance of the third inference model trained satisfies a condition; and outputting the third inference model trained, when the performance satisfies the condition.

Abstract: An information processing apparatus includes: an obtainer that obtains sensing data; a common neutral network (NN) that inputs the sensing data into an inference model to obtain a result of inference and information on a processing time for a plurality of tasks subsequent to the processing performed by the inference model; and an NN inference computation management unit that determines a task schedule for a task processing unit that processes the plurality of subsequent tasks to process the plurality of subsequent tasks on the basis of the information on the processing time for the plurality of subsequent tasks and inputs the result of the inference into the task processing unit to process the plurality of subsequent tasks according to the determined task schedule.

Abstract: Provided is a behavior recognition device that selects a candidate behavior as a recognition candidate from among target behaviors based on position information on an image sensor and room layout information, acquires image data detected by the image sensor, determines one or more recognizers corresponding to the candidate behavior, calculates a feature value of the image data using the one or more recognizers, and recognizes the candidate behavior based on the feature value.

Abstract: An information processing method includes: inputting sensing data into a first inference model to obtain a feature map that is a first inference result of the sensing data from the first inference model; inputting the first inference result into a third inference model to obtain, from the third inference model, model selection information indicating at least one second inference model that is selected from among a plurality of second inference models; inputting the first inference result into the at least one second inference model indicated by the model selection information to obtain, from the at least one second inference model, at least one second inference result of the first inference result, and outputting the at least one second inference result.

Abstract: An information processing device includes a processor. The processor obtains an input image, inputs the input image to a machine learning model that executes classification likelihood calculation processing to obtain, for each of candidate objects in the input image, likelihoods of belonging to the plurality of classes, executes first determination on whether or not each of the candidate objects is classified as a first class of the plurality of classes using a likelihood of belonging to the first class that is a likelihood having a negative correlation with likelihoods of belonging to other classes, executes second determination on whether or not each of the candidate objects that have been determined in the first determination as a non-first class is classified as the other classes, and outputting a result of classifying the candidate objects included in the input image using a result of the second determination.

Abstract: The present invention provides, in polishing an object to be polished that contains an (a) material having silicon-nitrogen bonding and (b) other materials, means that is capable of improving a ratio of a polishing speed of the (a) material to a polishing speed of the (b) materials. The present invention relates to a polishing composition used for polishing an object to be polished that contains an (a) material having silicon-nitrogen bonding and (b) other materials, the polishing composition containing: organic acid-immobilized silica; a dispersing medium; a selection ratio improver that improves a ratio of a polishing speed of the (a) material to a polishing speed of the (b) materials; and an acid, in which the selection ratio improver is organopolysiloxane having a hydrophilic group.

Abstract: An information processing device is an information processing device including a processor. The processor obtains a detection result of a first detector for detecting a first target in first sensing data; and based on the detection result of the first detector, determines a setting of processing by a second detector for detecting a second target in second sensing data next in an order after the first sensing data, the second target being different from the first target.

Abstract: First data is input to a first model to obtain a first result, the first data is input to a second model to obtain a second result, an error between discriminating information about the first result input to a discriminating model and correct answer information indicating an output of the first model is obtained, an error between discriminating information about the second result input to the discriminating model and correct answer information indicating an output of the second model is obtained, the discriminating model is trained by machine learning to reduce the errors, second data is input to the second model to obtain a third result, an error between discriminating information about the third result input to the discriminating model and correct answer information indicating an output of the first model is obtained, and the second model is trained by machine learning to reduce the error.

Abstract: An information processing includes: obtaining first data belonging to a first type and second data belonging to a second type different from the first type; calculating a first prediction result by inputting the first data into a first prediction model; calculating a second prediction result by inputting the first data into the second prediction model; calculating a third prediction result by inputting the second data into the second prediction model; calculating a first error between the first prediction result and the second prediction result; calculating a second error between the second prediction result and the third prediction result; and training the second prediction model by machine learning, based on the first error and the second error.

Abstract: An information processing method includes: obtaining first data; calculating a first prediction result by inputting the first data into a first prediction model; calculating a second prediction result by inputting the first data into a second prediction model; calculating a degree of similarity between the first prediction result and the second prediction result; determining second data which is training data for machine learning, based on the degree of similarity; and training the second prediction model by machine learning using the second data.

Abstract: An information processing device includes a processor. This processor: obtains at least one of first sensor data output from a first sensor and used to determine an ambient environment of a device in which a third sensor is placed, and second sensor data used to determine an orientation of this device; determines a tilt of a plane in a sensing direction of the third sensor with respect to the orientation of the device based on the at least one of the first sensor data and the second sensor data; determines, in accordance with the tilt determined, a processing target area of third sensor data output from the third sensor and used for object detection processing in the sensing direction of the third sensor; and executes the object detection processing using the processing target area determined, of the third sensor data.

Abstract: An information processing method includes: deciding a configuration of a machine learning model, using a processor; performing first determination as to whether the machine learning model in the decided configuration meets a first performance requirement on hardware performance; performing learning using the machine learning model in the configuration determined to meet the first performance requirement, performing second determination as to whether a learned model obtained by the learning meets a second performance requirement on evaluation value of output of a machine learning model, and when the learned model is determined to meet the second performance requirement, outputting information indicating that both performance requirements are met; and when it is determined that the first performance requirement is not met, changing the configuration of the machine learning model, and performing the first determination as to whether the machine learning model in the changed configuration meets the first performa

Abstract: An information processing method including the following executed using a computer: obtaining a neural network model that solves a regression problem; obtaining input data and label data corresponding to the input data; compressing a network of the neural network model to obtain a compressed model; and changing the label data and the number of nodes in the neural network model, based on information indicating performance of the compressed model, the number of nodes being assigned to the regression problem, the information being calculated using the label data and output data which is obtained by inputting the input data to the compressed model.

Abstract: An information processing method including the following executed using a computer: obtaining a neural network model that solves a regression problem; obtaining input data and label data corresponding to the input data; compressing a network of the neural network model to obtain a compressed model; transforming the regression problem to be solved by the neural network model into a classification problem, based on information indicating performance of the compressed model, the information being calculated using the label data and output data which is obtained by inputting the input data to the compressed model; and changing a network configuration of the neural network model and transforming the label data, in accordance with the transformation from the regression problem to the classification problem.

Abstract: An information processing device includes a processor. The processor obtains a first classification threshold for classifying data into at least one of a plurality of classes, and outputs a classification result of classifying the data into at least one of the plurality of classes based on an output of a trained classification model and the first classification threshold. The first classification threshold is obtained by a second transform performed on a second classification threshold, the second transform being an inverse transform of a first transform. The first transform corresponds to transforming the output of the trained classification model into a classification probability value of each of a plurality of unit classes constituting the plurality of classes. The second classification threshold is set based on the classification probability values of the plurality of unit classes.

Abstract: An information processing device is an information processing device including a processor. The processor obtains a detection result of a first detector for detecting a first target in first sensing data; and based on the detection result of the first detector, determines a setting of processing by a second detector for detecting a second target in second sensing data next in an order after the first sensing data, the second target being different from the first target.