Abstract: Systems and methods for providing a multi-step identification process using a hybrid edge/server infrastructure to generate machine learned inferences are provided. One example method includes obtaining sensor data of an environment including one or more user. The method includes selecting at least a portion of the sensor data that is associated with the one or more users. The method includes generating, using one or more machine learning models, an intermediate representation of the selected portion of the sensor data that represents the face of the one or more users. The method includes transmitting the intermediate representation and a request for a user identification based on the intermediate representation, the remote computing system configured to perform additional steps of the multi-step identification process using the intermediate representation and the machine-learned system. The method includes receiving a user identifier for the one or more users.

Abstract: Systems and methods for graph model search and/or for architecture insight can include training and testing a plurality of graph models. For example, the systems and methods can generate a plurality of synthetic graph datasets, which can then be utilized to train a plurality of graph models with varying graph model architectures. The trained graph models can then be evaluated based on outputs generated by the models based on test inputs. The evaluation data can then be utilized for providing particular graph model insight and/or may be utilized to enable task-specific graph model search.

Abstract: Systems and methods for providing a multi-step identification process using a hybrid edge/server infrastructure to generate machine learned inferences are provided. One example method includes obtaining sensor data of an environment including one or more user. The method includes selecting at least a portion of the sensor data that is associated with the one or more users. The method includes generating, using one or more machine learning models, an intermediate representation of the selected portion of the sensor data that represents the face of the one or more users. The method includes transmitting the intermediate representation and a request for a user identification based on the intermediate representation, the remote computing system configured to perform additional steps of the multi-step identification process using the intermediate representation and the machine-learned system. The method includes receiving a user identifier for the one or more users.

Abstract: Systems and methods for providing a multi-step identification process using a hybrid edge/server infrastructure to generate machine learned inferences are provided. One example method includes obtaining sensor data of an environment including one or more user. The method includes selecting at least a portion of the sensor data that is associated with the one or more users. The method includes generating, using one or more machine learning models, an intermediate representation of the selected portion of the sensor data that represents the face of the one or more users. The method includes transmitting the intermediate representation and a request for a user identification based on the intermediate representation, the remote computing system configured to perform additional steps of the multi-step identification process using the intermediate representation and the machine-learned system. The method includes receiving a user identifier for the one or more users.

Abstract: Systems and methods for providing a multi-step identification process using a hybrid edge/server infrastructure to generate machine learned inferences are provided. One example method includes obtaining sensor data of an environment including one or more user. The method includes selecting at least a portion of the sensor data that is associated with the one or more users. The method includes generating, using one or more machine learning models, an intermediate representation of the selected portion of the sensor data that represents the face of the one or more users. The method includes transmitting the intermediate representation and a request for a user identification based on the intermediate representation, the remote computing system configured to perform additional steps of the multi-step identification process using the intermediate representation and the machine-learned system. The method includes receiving a user identifier for the one or more users.

Abstract: A method can include determining a distance of a person's face from a camera that captured an image of the person's face, determining a size of a display in communication with the camera, and scaling the image based on the determined distance of the person's face and the determined size of the display.

Abstract: A method can include determining a distance of a person's face from a camera that captured an image of the person's face, determining a size of a display in communication with the camera, and scaling the image based on the determined distance of the person's face and the determined size of the display.

Abstract: An architecture and methodology provides a uniform approach for reconciling accounts in a manner that ensures proper controls, both legal and financial, are being met. In the described implementation, the architecture offers a web-based solution that facilitates online account reconciliation in a central and controlled method across many different divisions and business units, located within in the U.S. or around the world. The system architecture enables creation and maintenance of reconciliation profiles that define how different accounts are to be reconciled. The system architecture further supports creation of reconciliation documentations, based on the reconciliation profiles, which are used to reconcile individual accounts.

Abstract: An architecture and methodology provides a uniform approach for reconciling accounts in a manner that ensures proper controls, both legal and financial, are being met. In the described implementation, the architecture offers a web-based solution that facilitates online account reconciliation in a central and controlled method across many different divisions and business units, located within in the U.S. or around the world. The system architecture enables creation and maintenance of reconciliation profiles that define how different accounts are to be reconciled. The system architecture further supports creation of reconciliation documentations, based on the reconciliation profiles, which are used to reconcile individual accounts.