Abstract: Introduced here is a network-accessible platform (or simply “platform”) that is designed to monitor digital activities that are performed across different services to ascertain, in real time, threats to the security of an enterprise. In order to surface insights into the threats posed to an enterprise, the platform can apply machine learning models to data that is representative of digital activities performed on different services with respective accounts. Each model may be trained to understand what constitutes normal behavior for a corresponding employee with respect to a single service or multiple services. Not only can these models be autonomously trained for the employees of the enterprise, but they can also be autonomously applied to detect, characterize, and catalog those digital activities that are indicative of a threat.

Abstract: Introduced here is a network-accessible platform (or simply “platform”) that is designed to monitor digital activities that are performed across different services to ascertain, in real time, threats to the security of an enterprise. In order to surface insights into the threats posed to an enterprise, the platform can apply machine learning models to data that is representative of digital activities performed on different services with respective accounts. Each model may be trained to understand what constitutes normal behavior for a corresponding employee with respect to a single service or multiple services. Not only can these models be autonomously trained for the employees of the enterprise, but they can also be autonomously applied to detect, characterize, and catalog those digital activities that are indicative of a threat.

Abstract: Introduced here is a network-accessible platform (or simply “platform”) that is designed to monitor digital activities that are performed across different services to ascertain, in real time, threats to the security of an enterprise. In order to surface insights into the threats posed to an enterprise, the platform can apply machine learning models to data that is representative of digital activities performed on different services with respective accounts. Each model may be trained to understand what constitutes normal behavior for a corresponding employee with respect to a single service or multiple services. Not only can these models be autonomously trained for the employees of the enterprise, but they can also be autonomously applied to detect, characterize, and catalog those digital activities that are indicative of a threat.

Abstract: Introduced here is a network-accessible platform (or simply “platform”) that is designed to monitor digital activities that are performed across different services to ascertain, in real time, threats to the security of an enterprise. In order to surface insights into the threats posed to an enterprise, the platform can apply machine learning models to data that is representative of digital activities performed on different services with respective accounts. Each model may be trained to understand what constitutes normal behavior for a corresponding employee with respect to a single service or multiple services. Not only can these models be autonomously trained for the employees of the enterprise, but they can also be autonomously applied to detect, characterize, and catalog those digital activities that are indicative of a threat.

Abstract: Method(s), apparatus, and system(s) are provided for selecting a data model configuration for use in training predictive models comprise receiving two or more data model configurations, extracting a data model for each of the two or more data model configurations from a knowledge graph, generating a separate predictive model for each of the extracted data models, scoring the output of each separate predictive model based on a benchmark data set, and selecting at least one data model configuration of the two or more data model configurations based on the output scores.

Abstract: A computer-implemented method of electronically mining medical and scientific datasets to determine a ranking indicating a level of evidence for an association between two entities is disclosed. The method comprises receiving a representation of an entity pair, performing first data mining on one or more unstructured datasets to generate one or more first scores each representing an extent of association between the entities of the entity pair, and performing second data mining on one or more structured datasets to generate one or more second scores each representing an extent of association between the entities of the entity pair. The method also comprises using a classifier to determine a predicted ranking for the entity pair using the one or more first scores and the one or more second scores, and providing the predicted ranking to a user as an indication of the strength of evidence for an association between the entities of the entity pair.

Abstract: Link prediction for biomedical entities. A neural network is trained using known associations between biomedical entities, including their vector representations and additional information-carrying content describing the biomedical entities. The trained network infers or predicts unobserved associations between two entities.