A model analyzer may receive a representative data set as input and select one of a plurality of analytic models to perform the analysis. Before deciding which model to use the model may be trained, and the trained model evaluated for accuracy. However, some models are known to behave poorly when the training data is distributed in a particular way. Thus, the cost of training a model and evaluating the trained model can be avoided by first analyzing the distribution of the representative data. Identifying the representative data distribution allows ruling out use of models for which the distribution of the representative data is unsuitable. Only models that may be compatible with the distribution of the representative data may be trained and evaluated for accuracy. The most accurate trained model whose accuracy meets an accuracy threshold may be selected to analyze subsequently received data related to the representative data.

The analyzing apparatus: generates first internal data; converts a position of first feature data in a feature space, based on the first internal data and a second learning parameter; reallocates, based on a result of first conversion and the first feature data, the first feature data to a position obtained through the conversion in the feature space; calculates a predicted value of a hazard function of analysis time in a case where the first feature data is given, based on a result of reallocation and a third learning parameter; optimizes the first to third learning parameters, based on a response variable and a first predicted value; generates second internal data, based on second feature data and the optimized first learning parameter; converts a position of the second feature data in the feature space, based on the second internal data and the optimized second learning parameter; and calculates importance data.

A method for training a neural network for triggering an input signal in a measurement device is provided. The method comprises the steps of providing a trigger type and/or trigger parameter from a cloud server hosting the neural network via a network to the measurement device, triggering the input signal based on the trigger type and/or trigger parameter received in the measurement device, and collecting trigger feedback information from the measurement device at the neural network to train the neural network.

The present disclosure describes a computer-implemented method for processing clinical three-dimensional image. The method includes training a fully supervised segmentation model using a labelled image dataset containing images for a disease at a predefined set of contrast phases or modalities, allow the segmentation model to segment images at the predefined set of contrast phases or modalities; finetuning the fully supervised segmentation model through co-heterogenous training and adversarial domain adaptation (ADA) using an unlabelled image dataset containing clinical multi-phase or multi-modality image data, to allow the segmentation model to segment images at contrast phases or modalities other than the predefined set of contrast phases or modalities; and further finetuning the fully supervised segmentation model using domain-specific pseudo labelling to identify pathological regions missed by the segmentation model.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to train a generative adversarial network (GAN) to reconstruct a missing portion of an image by determining a reconstructed portion of the image based on data from portions of the image surrounding the missing portion and compare an acquired image with the reconstructed portion of the image to determine a damaged portion. The instructions further include instructions to determine estimated damage based on the damaged portion.

Certain aspects of the present disclosure provide techniques for updating a policy of an agent, including receiving a first transaction file associated with an entity; predicting, by the agent, an expected reward for each respective string of a plurality of strings associated with the first transaction file based on a policy of the agent, wherein the policy is determined based on a context comprising at least an attribute of the entity; determining a first string based on a highest expected reward; providing, to an environment, the first string; receiving a response to the first string, wherein the response comprises an actual reward; and updating the policy of the agent based on the response to the first string.

Interfaces and systems are provided for harvesting ground truth from forms to be used in training models based on key-value pairings in the forms and to later use the trained models to identify related key-value pairings in new forms. Initially, forms are identified and clustered to identify a subset of forms to label with the key-value pairings. Users provide input to identify keys to use in labeling and then select/highlight text from forms that are presented concurrently with the keys in order to associate the highlighted text with the key(s) as the corresponding key-value pairing(s). After labeling the forms with the key-value pairings, the key-value pairing data is used as ground truth for training a model to independently identify the key-value pairing(s) in new forms. Once trained, the model is used to identify the key-value pairing(s) in new forms.

Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.

A system and method for processing loans includes a machine learning model associated with processing loans. The machine learning model may be configured based on an objective function associated with loan processing. One or more weights of the objective function may be updated to account for changes in one or more business conditions. The machine learning model may be configured based on the updates to the one or more weights.

A non-transitory computer-readable storage medium storing a model generation program that causes a computer to execute a process includes generating a plurality of first coefficient matrixes representing a relationship between a first observation matrix that has a feature and a characteristic vector that has a characteristic value of each of the plurality by a regression coefficient; generating a histogram in which a plurality of total regression coefficients obtained by totaling the regression coefficient included in the plurality of first coefficient matrixes for each of the plurality of elements is arranged in order of element in the first observation matrix; generating a second observation matrix including a second element acquired by combining a plurality of first elements that corresponds to the adjacent total regression coefficients of nonzero in the histogram into one; and generating a second coefficient matrix representing a relationship between the second observation matrix and the characteristic vector.