Disclosed is an automatic image classification and processing method based on the continuous processing structure of multiple artificial intelligence models. An automatic image classification and processing method based on a continuous processing structure of multiple artificial intelligence models includes receiving image data, generating a first feature extraction value by inputting the image data into a first feature extraction model among feature extraction models, generating a second feature extraction value by inputting the image data into a second feature extraction model among the feature extraction models, and determining a classification value of the image data by inputting the first and second feature extraction values into a classification model.

Embodiments of the present disclosure relate to methods, systems, and computer program products for observation data evaluation. In a method, a hierarchical relationship between a plurality of observation items is obtained based on a dataset including a plurality of observation samples. Here, an observation sample in the plurality of observation samples includes a group of measurements for the group of observation items, respectively. A plurality of evaluation models for evaluating an observation sample is generated based on the hierarchical relationship according to a predefined group of membership functions and a predefined group of fuzzy operators. An evaluation model is selected for a further evaluation from the plurality of evaluation models based on a plurality of confidence intervals for the plurality of evaluation models. With these embodiments, the evaluation model may be obtained in an easy and more effective way.

A stabilization method and mechanism for model structure learning is described. A model is built based on a full data set. The full data set is partitioned into cross validation (CV) folds. A set of model structures of the model are cross validated for each CV fold while penalizing structural deviations from the model to determine CV errors. A model structure is selected from the set of model structures based on a comparison of CV errors with an industrial data set.

A method of estimating employee turnover rates obtains original employee data from a preset data source. First data processing is applied to the original employee data to obtain first processed employee data. A training set and a first verification set are selected from the first processed employee data. The training set is used to train a machine learning model to obtain a turnover estimation model. The first verification set is used to verify the turnover estimation model to obtain a first estimation result. The turnover estimation model is optimized according to the first estimation result to obtain an optimized turnover estimation model. Updated employee data and a corresponding employee turnover rate of a second time period are obtained. The method helps to replenish manpower in time and avoids over-recruitment.

An image processing method includes a first device extracting feature information of a to-be-processed image using a feature extraction network model; the first device identifying the extracted feature information to obtain identification information of the feature information; and the first device sending the feature information of the to-be-processed image and the identification information of the feature information to a second device. After receiving the feature information and the corresponding identification information that are sent by the first device, the second device selects a feature analysis network model corresponding to the identification information to process the received feature information.

A method for automated machine learning includes controlling execution of a plurality of instantiations of different automated machine learning frameworks on a machine learning task each as a separate arm in consideration of available computational resources and time budget. During the execution by the separate arms, a plurality of machine learning models are trained and performance scores of the plurality of trained machine learning models are computed such that one or more of the plurality of trained machine learning models are selectable for the machine learning task based on the performance scores. This invention can be used for predicting patient discharge, predictive control in buildings for energy optimization, and so on.

Methods, systems, and apparatus for efficient image analysis. In some aspects, a system includes a camera configured to capture images, one or more environment sensors configured to detect movement of the camera, a data processing apparatus, and a memory storage apparatus in data communication with the data processing apparatus. The data processing apparatus can access, for each of a multitude of images captured by a mobile device camera, data indicative of movement of the camera at a time at which the camera captured the image. The data processing apparatus can also select, from the images, a particular image for analysis based on the data indicative of the movement of the camera for each image, analyze the particular image to recognize one or more objects depicted in the particular image, and present content related to the one or more recognized objects.

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 learning system includes processing circuitry. The processing circuitry is configured to acquire a first data distribution for a first data set out of data sets based on a first cohort, to select a second cohort that is used to update a first model out of a plurality of second cohorts on the basis of the acquired first data distribution, and to update the first model on the basis of at least part of a second data set out of data sets based on the selected second cohort.

A training device and a training method for training a multi-goal model based on goals in a goal space are provided. The training device includes a memory and a processor coupled to the memory. The processor is configured to set the goal space, to acquire a plurality of sub-goal spaces of different levels of difficulty; change a sub-goal space to be processed from a current sub-goal space to a next sub-goal space of a higher level of difficulty; select, as sampling goals, goals at least from the current sub-goal space, and to acquire transitions related to the sampling goals by executing actions; train the multi-goal model based on the transitions, and evaluate the multi-goal model by calculating a success rate for achieving goals in the current sub-goal space.