Abstract: In various examples there is a computer-implemented method of database construction. The method comprises storing a knowledge graph comprising nodes connected by edges, each node representing a topic. Accessing a topic type hierarchy comprising a plurality of types of topics, the topic type hierarchy having been computed from a corpus of text documents. One or more text documents are accessed and the method involves labelling a plurality of the nodes with one or more labels, each label denoting a topic type from the topic type hierarchy, by, using a deep language model; or for an individual one of the nodes representing a given topic, searching the accessed text documents for matches to at least one template, the template being a sequence of words and containing the given topic and a placeholder for a topic type; and storing the knowledge graph comprising the plurality of labelled nodes.

Abstract: In various examples there is a computer-implemented method of database construction. The method comprises storing a knowledge graph comprising nodes connected by edges, each node representing a topic. Accessing a topic type hierarchy comprising a plurality of types of topics, the topic type hierarchy having been computed from a corpus of text documents. One or more text documents are accessed and the method involves labelling a plurality of the nodes with one or more labels, each label denoting a topic type from the topic type hierarchy, by, using a deep language model; or for an individual one of the nodes representing a given topic, searching the accessed text documents for matches to at least one template, the template being a sequence of words and containing the given topic and a placeholder for a topic type; and storing the knowledge graph comprising the plurality of labelled nodes.

Abstract: Checking and/or completing for data grids is described such as for grids having rows and columns of cells at least some of which contain data values such as numbers or categories. In various embodiments predictive probability distributions are obtained from an inference engine for one or more of the cells and the predictive probability distributions are used for various tasks such as to suggest values to complete blank cells, highlight cells having outlying values, identify potential errors, suggest corrections to potential errors, identify similarities between cells, identify differences between cells, cluster rows of the data grid, and other tasks. In various embodiments a graphical user interface displays a data grid and provides facilities for completing, error checking/correcting, and analyzing data in the data grid.

Abstract: An inference engine is described for efficient machine learning. For example, an inference engine executes a plurality of ordered steps to carry out inference on the basis of observed data. For each step, a plurality of inputs to the step are received. A predictor predicts an output of the step and computes uncertainty of the prediction. Either the predicted output or a known output is selected on the basis of the uncertainty. If the known output is selected, the known output is computed, (for example, using a resource intensive, accurate process). The predictor is retrained using the known output and the plurality of inputs of the step as training data. For example, computing the prediction is fast and efficient as compared with computing the known output.

Abstract: Technologies are generally provided far a hierarchical, feature teed statistical model that cm be used for personalized classification or predictions within a community of users. Personalization refers to learning about the habits and characteristics of individual users and adapting user experiences based on that learning. The model may be used in a communication application to predict user actions on incoming email messages and to help users triage email by making personalized suggestions based on the model predictions. A community of users associated together with the communication application may be incorporated together into a single model to enable for continuous fine-grain interaction between intelligence learned from the community of users as a whole and that learned from individual users.

Abstract: An inference engine is described for efficient machine learning. For example, an inference engine executes a plurality of ordered steps to carry out inference on the basis of observed data. For each step, a plurality of inputs to the step are received. A predictor predicts an output of the step and computes uncertainty of the prediction. Either the predicted output or a known output is selected on the basis of the uncertainty. If the known output is selected, the known output is computed, (for example, using a resource intensive, accurate process). The predictor is retrained using the known output and the plurality of inputs of the step as training data. For example, computing the prediction is fast and efficient as compared with computing the known output.

Abstract: A system is provided for automatically predicting actions a user is likely to take in response to receiving data. The system may be configured to monitor and observe a user's interactions with incoming data and to identify patterns of actions the user may take in response to the incoming data. The system may enable a trainer component and a classifier component to determine the probability a user may take a particular action and to make predictions of likely user actions based on the observations of the user and the identified pattern of the user's actions. The system may also be configured to continuously observe the user's actions to fine-tune and adjust the identified patterns of user actions and to update the probabilities of likely user actions in order increase the accuracy of the predicted user action in response to incoming data.

Abstract: Systems and methods for editing digital images using information about objects in those images are described. For example, the information about objects comprises depth ordering information and/or information about the class each object is a member of. Examples of classes include sky, building, aeroplane, grass and person. This object-level information is used to provide new and/or improved editing functions such as cut and paste, filling-in image regions using tiles or patchworks, digital tapestry, alpha matte generation, super resolution, auto cropping, auto color balance, object selection, depth of field manipulation, and object replacement. In addition improvements to user interfaces for image editing systems are described which use object-level information.

Abstract: Technologies are generally provided for a prediction system to provide reasons corresponding to suggested classifications. The prediction system may predict classifications such as user actions on incoming messages to help users triage email, and may provide one or more reasons for classifications to a user. The prediction system may identify features of the message in order to make predictions about user interactions and to suggest an action to the user, where features may include characteristics of the email message such as sender identity. Presented reasons for a suggested action may convey observed features of the message that significantly contributed to the prediction decision, and were relatively unexpected compared to a typical item for a particular user.

Abstract: Technologies are generally provided far a hierarchical, feature teed statistical model that cm be used for personalized classification or predictions within a community of users. Personalization refers to learning about the habits and characteristics of individual users and adapting user experiences based on that learning. The model may be used in a communication application to predict user actions on incoming email messages and to help users triage email by making personalized suggestions based on the model predictions. A community of users associated together with the communication application may be incorporated together into a single model to enable for continuous fine-grain interaction between intelligence learned from the community of users as a whole and that learned from individual users.

Abstract: A system is provided for automatically predicting actions a user is likely to take in response to receiving data. The system may be configured to monitor and observe a user's interactions with incoming data and to identify patterns of actions the user may take in response to the incoming data. The system may enable a trainer component and a classifier component to determine the probability a user may take a particular action and to make predictions of likely user actions based on the observations of the user and the identified pattern of the user's actions. The system may also be configured to continuously observe the user's actions to fine-tune and adjust the identified patterns of user actions and to update the probabilities of likely user actions in order increase the accuracy of the predicted user action in response to incoming data.

Abstract: A system is provided for automatically predicting actions a user is likely to take in response to receiving data. The system may be configured to monitor and observe a user's interactions with incoming data and to identify patterns of actions the user may take in response to the incoming data. The system may enable a trainer component and a classifier component to determine the probability a user may take a particular action and to make predictions of likely user actions based on the observations of the user and the identified pattern of the user's actions. The system may also be configured to continuously observe the user's actions to fine-tune and adjust the identified patterns of user actions and to update the probabilities of likely user actions in order increase the accuracy of the predicted user action in response to incoming data.

Abstract: Triaging electronic communications in a computing system environment can mitigate issues related to large volumes of incoming electronic communications. This can include an analysis of user-specific electronic communication data and associated behaviors to predict which communications a user is likely to deem important or unimportant. Client-side application features are exposed based on the evaluation of communication importance to enable the user to process arbitrarily large volumes of incoming communications.

Abstract: A system and method for detecting and tracking targets including body parts and props is described. In one aspect, the disclosed technology acquires one or more depth images, generates one or more classification maps associated with one or more body parts and one or more props, tracks the one or more body parts using a skeletal tracking system, tracks the one or more props using a prop tracking system, and reports metrics regarding the one or more body parts and the one or more props. In some embodiments, feedback may occur between the skeletal tracking system and the prop tracking system.

Abstract: Checking and/or completing for data grids is described such as for grids having rows and columns of cells at least some of which contain data values such as numbers or categories. In various embodiments predictive probability distributions are obtained from an inference engine for one or more of the cells and the predictive probability distributions are used for various tasks such as to suggest values to complete blank cells, highlight cells having outlying values, identify potential errors, suggest corrections to potential errors, identify similarities between cells, identify differences between cells, cluster rows of the data grid, and other tasks. In various embodiments a graphical user interface displays a data grid and provides facilities for completing, error checking/correcting, and analyzing data in the data grid.

Abstract: A system is provided for automatically notifying a user of predicted action. The system may be configured to monitor and observe a user's interactions with incoming data, identify patterns of actions the user may take in response to the incoming data and generate a notification associated with the action. A trainer component and a classifier component determine the probability a user may take a particular action and to make predictions of likely user actions based on the observations of the user. A notifier may communicate with the classifier to generate a particular user notification associated with a user action response generated by the classifier. The notifier component utilizes a logic device to compare the received user prediction from the classifier with a plurality of user notifications stored in a database. The notifier component sends the user notification to one or more user devices associated with a user.

Abstract: A system is provided for automatically predicting actions a user is likely to take in response to receiving data. The system may be configured to monitor and observe a user's interactions with incoming data and to identify patterns of actions the user may take in response to the incoming data. The system may enable a trainer component and a classifier component to determine the probability a user may take a particular action and to make predictions of likely user actions based on the observations of the user and the identified pattern of the user's actions. The system may also be configured to continuously observe the user's actions to fine-tune and adjust the identified patterns of user actions and to update the probabilities of likely user actions in order increase the accuracy of the predicted user action in response to incoming data.

Abstract: Data processing using restricted Boltzmann machines is described, for example, to pre-process continuous data and provide binary outputs. In embodiments, restricted Boltzmann machines based on either Gaussian distributions or Beta distributions are described which are able to learn and model both the mean and variance of data. In some embodiments, a stack of restricted Boltzmann machines are connected in series with outputs of one restricted Boltzmann machine providing input to the next in the stack and so on. Embodiments describe how training for each machine in the stack may be carried out efficiently and the combined system used for one of a variety of applications such as data compression, object recognition, image processing, information retrieval, data analysis and the like.

Abstract: Image processing using masked restricted Boltzmann machines is described. In an embodiment restricted Boltzmann machines based on beta distributions are described which are implemented in an image processing system. In an embodiment a plurality of fields of masked RBMs are connected in series. An image is input into a masked appearance RBM and decomposed into superpixel elements. The superpixel elements output from one appearance RBM are used as input to a further appearance RBM. The outputs from each of the series of fields of RBMs are used in an intelligent image processing system. Embodiments describe training a plurality of RBMs. Embodiments describe using the image processing system for applications such as object recognition and image editing.

Abstract: Triaging electronic communications in a computing system environment can mitigate issues related to large volumes of incoming electronic communications. This can include an analysis of user-specific electronic communication data and associated behaviors to predict which communications a user is likely to deem important or unimportant. Client-side application features are exposed based on the evaluation of communication importance to enable the user to process arbitrarily large volumes of incoming communications.