Abstract: A model generation method according to one aspect of the present invention acquires first data and second data regarding a crystal structure of a material, and performs machine learning for a first encoder and a second encoder by using the first data and the second data. The second data indicates a property of the material with an index different from that of the first data. The first encoder is configured to convert the first data into a first feature vector, and the second encoder is configured to convert the second data into a second feature vector. The dimension of the first feature vector is the same as the dimension of the second feature vector. In machine learning, the first encoder and the second encoder are trained so that the values of the feature vectors of the positive samples are positioned close to each other, and the feature vector of the negative sample is positioned far from the feature vector of the positive sample.

Abstract: A prediction model (1) includes a first module (M1) that calculates, for each of a plurality of objects (xi) in a dataset (x), an index value (vi) corresponding to a combination of the object (xi) and attribute information (a) using a neural network, and a second module (M2) that calculates a prediction result (y) of an operation to be performed by a user by performing a predetermined process on a plurality of index values (v1, . . . , vN) obtained from the first module (M1) and corresponding to the respective plurality of objects (x1, . . . , xN).

Abstract: A model generation apparatus trains, through machine learning, a neural network module that includes an extraction operation to extract an element satisfying a predetermined condition from a set of targets. In the machine learning, the model generation apparatus performs the extraction operation in a phase of forward propagation with the neural network module, and replaces, in a phase of backpropagation, the extraction operation with a differentiable alternative operation and differentiates the alternative operation to compute an approximate gradient corresponding to differentiation for the extraction operation.

Abstract: A computing system is provided, including a processor configured to receive a labeling map for a first image. The labeling map may indicate a spatial relationship between a first region of interest included in the first image and a second region of interest included in a second image. At a trained reinforcement learning model, the processor may be further configured to generate an updated labeling map for the first image based on at least the labeling map, the first image, and the second image.

Abstract: A computing system is provided, including a processor configured to receive a labeling map for a first image. The labeling map may indicate a spatial relationship between a first region of interest included in the first image and a second region of interest included in a second image. At a trained reinforcement learning model, the processor may be further configured to generate an updated labeling map for the first image based on at least the labeling map, the first image, and the second image.