Abstract: Provided are systems for ensemble learning with machine learning models that include a processor to receive a training dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, add an amount of time delay to one or more data instances to provide an augmented training dataset, select a first plurality of supervised machine learning models, select a second plurality of unsupervised machine learning models, train the first plurality of supervised machine learning models and the second plurality of unsupervised machine learning models based on the augmented training dataset, generate an ensemble machine learning model based on outputs of the supervised machine learning models and unsupervised machine learning models, and generate a runtime output of the ensemble machine learning model based on a runtime input to the ensemble machine learning model. Methods and computer program products are also provided.

Abstract: Systems, methods, and computer program products that use unsupervised learning to learn relationships between operations of a machine learning model based on a model graph representation to group the operations into clusters and, given a set of clusters and labels for the clusters, use a reinforcement learning algorithm to generate a final device placement result for the machine learning model.

Abstract: Provided are systems for ensemble learning with machine learning models that include a processor to receive a training dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, add an amount of time delay to one or more data instances to provide an augmented training dataset, select a first plurality of supervised machine learning models, select a second plurality of unsupervised machine learning models, train the first plurality of supervised machine learning models and the second plurality of unsupervised machine learning models based on the augmented training dataset, generate an ensemble machine learning model based on outputs of the supervised machine learning models and unsupervised machine learning models, and generate a runtime output of the ensemble machine learning model based on a runtime input to the ensemble machine learning model. Methods and computer program products are also provided.

Abstract: Provided are systems for ensemble learning with machine learning models that include a processor to receive a training dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, add an amount of time delay to one or more data instances to provide an augmented training dataset, select a first plurality of supervised machine learning models, select a second plurality of unsupervised machine learning models, train the first plurality of supervised machine learning models and the second plurality of unsupervised machine learning models based on the augmented training dataset, generate an ensemble machine learning model based on outputs of the supervised machine learning models and unsupervised machine learning models, and generate a runtime output of the ensemble machine learning model based on a runtime input to the ensemble machine learning model. Methods and computer program products are also provided.

Abstract: Provided are systems for ensemble learning with machine learning models that include a processor to receive a training dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, add an amount of time delay to one or more data instances to provide an augmented training dataset, select a first plurality of supervised machine learning models, select a second plurality of unsupervised machine learning models, train the first plurality of supervised machine learning models and the second plurality of unsupervised machine learning models based on the augmented training dataset, generate an ensemble machine learning model based on outputs of the supervised machine learning models and unsupervised machine learning models, and generate a runtime output of the ensemble machine learning model based on a runtime input to the ensemble machine learning model. Methods and computer program products are also provided.

Abstract: Systems and methods for enhancing a consumer experience using shared data is provided. The system includes a shared data server in communication with one or more merchant sites and a remote server that may include a payment processing server. The shared data server receives, stores, and analyzes information provided from the merchant sites and the remote server to provide recommendations, offers, and the like to the consumer to enhance their experience. The shared data server may also provide upselling opportunities for the merchant site based on its analysis as well as opportunities to extend credit to consumers for the remote server based on its analysis of the received information. The shared data server may allow merchant sites and remote servers to make indirect use of the information that each has individually about products and customers without providing direct access to the information.

Abstract: Broadly speaking, the present invention fills the need of selecting correlated advertisements for displaying to users by utilizing the advertisement-viewing data collected on users of a web site. The advertisement-viewing (ad-viewing) data of users of a web site can be correlated to extract similarities and patterns of ads being viewed by users of the web site. The correlated ad-viewing data of all users of the web site, along with URLs (Uniform Resource Locators) of ads viewed by a particular user, can be used to select advertisements that are likely to be interests to the particular user. Ad-viewing data correlation allows the web site to select ads to display to users in real-time based on users' latest ad-viewing data. Since selection of ads to display is based on mathematical calculation using a correlation table of ad-viewing that has been generated ahead of time, the method can be scaled to meet demands of a large amount of users, such as millions.