The present disclosure relates to an information processing apparatus, an information processing method, and a program that make it possible to adjust commonness and eccentricity of automatically generated content by likelihood exploration while satisfying reality.

Input content including a sequence of data is encoded to be converted into a latent variable, the latent variable is decoded to reconfigure output content, a loss function is calculated on the basis of a likelihood of the input content which is an input sequence, a gradient of the loss function is lowered to update the latent variable, and the updated latent variable is decoded to reconfigure output content. The present invention can be applied to an automatic content generation device.

Disclosed subject matter relates generally to forming a set of training parameters applicable to detection of two or more entities between and/or among a distribution of entities from a plurality of digitally stored observations. One or more training parameters of the set of training parameters may be modified to define a translation, which is applicable to detection of real-world entities corresponding to the two or more entities in the distribution of the digitally stored observations, wherein the forming of the translation is to be based, at least in part, on a first process to generate the two or more entities in the distribution of digitally stored observations and a second process to discriminate between and/or among the generated two or more entities based, at least in part, on the modified one or more training parameters

The present disclosure provides a method and apparatus for acquiring a pre-trained model, an electronic device and a storage medium, and relates to the field of artificial intelligence, such as the natural language processing field, the deep learning field, or the like. The method may include: adding, in a process of training a pre-trained model using training sentences, a learning objective corresponding to syntactic information for a self-attention module in the pre-trained model; and training the pre-trained model according to the defined learning objective. The solution of the present disclosure may improve a performance of the pre-trained model, and reduce consumption of computing resources, or the like.

A non-transitory computer-readable storage medium storing an estimation program that causes at least one computer to execute a process, the process includes inputting an input data into a trained variational autoencoder that includes an encoder and a decoder; converting, into a first probability distribution, a probability distribution of a latent variable that is generated by the trained variational autoencoder according to the input based on a magnitude of a standard deviation output from the encoder; converting the first probability distribution into a second probability distribution based on an output error of the decoder regarding the input data; and outputting the second probability distribution as an estimated value of a probability distribution of the input data.

An analysis device includes a processor configured to: execute a first learning process on a generative model for images such that the images that bring a recognition result of an image recognition process into a preassigned state are generated; execute a second learning process on the generative model on which the first learning process has been executed, while gradually changing recognition accuracy of the images generated by the generative model on which the first learning process has been executed, to desired recognition accuracy; acquire each piece of information on back-error propagation calculated by executing the image recognition process, for the images with each level of the recognition accuracy generated through a course of the second learning process; and generate evaluation information indicating each of image parts that cause erroneous recognition at each level of the recognition accuracy, based on the acquired each piece of the information on the back-error propagation.

A computer-implemented method of machine-learning is described that includes obtaining a dataset of virtual scenes. The dataset of virtual scenes belongs to a first domain. The method further includes obtaining a test dataset of real scenes. The test dataset belongs to a second domain. The method further includes determining a third domain. The third domain is closer to the second domain than the first domain in terms of data distributions. The method further includes learning a domain-adaptive neural network based on the third domain. The domain-adaptive neural network is a neural network configured for inference of spatially reconfigurable objects in a real scene. Such a method constitutes an improved method of machine learning with a dataset of scenes including spatially reconfigurable objects.

A system and a method for classifying sensitive data elements in a file is provided. The method includes receiving and converting, the unstructured data file into a machine-readable format and generating, a plurality of sensitive data features. The plurality of sensitive data features represents single element of the sensitive data. The method includes generating, a plurality of adjacent elements corresponding to the single elements of the sensitive data and generating a plurality of feature categories. The method includes aggregating, the plurality of adjacent node features and the plurality of edge features. The method includes calculating and concatenating the plurality of aggregated adjacent nodes features and the plurality of aggregated edge features. The method includes comparing, the distance of the sensitive data from all of the adjacent sensitive data. The method includes classifying and predicting, the sensitive data to be a true positive or false positive sensitive data by using machine learning.

A system and a method for classifying sensitive data elements in a file is provided. The method includes receiving and converting, the unstructured data file into a machine-readable format and generating, a plurality of sensitive data features. The plurality of sensitive data features represents single element of the sensitive data. The method includes generating, a plurality of adjacent elements corresponding to the single elements of the sensitive data and generating a plurality of feature categories. The method includes aggregating, the plurality of adjacent node features and the plurality of edge features. The method includes calculating and concatenating the plurality of aggregated adjacent nodes features and the plurality of aggregated edge features. The method includes comparing, the distance of the sensitive data from all of the adjacent sensitive data. The method includes classifying and predicting, the sensitive data to be a true positive or false positive sensitive data by using machine learning.

Systems and methods for weak supervision classification with probabilistic generative latent variable models are disclosed. A method for weak supervision classification with probabilistic generative latent variable models may include: (1) receiving, by a generative model computer program, a plurality of records from a database; (2) receiving, by the generative model computer program, a plurality of user-defined label functions; (3) labeling, by the generative model computer program, each of the plurality of records with each of the plurality of user-defined label functions; (4) representing, by the generative model computer program, the plurality of records that are labeled with the user-defined label functions in a matrix; (5) performing, by the generative model computer program, probabilistic latent variable model analysis on the matrix using a probabilistic generative latent variable model; and (6) outputting, by the generative model computer program, a labeled dataset for the plurality of records.

Systems and methods for weak supervision classification with probabilistic generative latent variable models are disclosed. A method for weak supervision classification with probabilistic generative latent variable models may include: (1) receiving, by a generative model computer program, a plurality of records from a database; (2) receiving, by the generative model computer program, a plurality of user-defined label functions; (3) labeling, by the generative model computer program, each of the plurality of records with each of the plurality of user-defined label functions; (4) representing, by the generative model computer program, the plurality of records that are labeled with the user-defined label functions in a matrix; (5) performing, by the generative model computer program, probabilistic latent variable model analysis on the matrix using a probabilistic generative latent variable model; and (6) outputting, by the generative model computer program, a labeled dataset for the plurality of records.