Abstract: A system includes a machine learning model configured to, based on textual representations of queries, classify the queries among query intents, which may be mapped to predetermined solutions to problems. The system also includes a software application configured to receive a query that includes a textual representation of a problem, and generate, by the machine learning model and based on the textual representation of the query, a query intent therefor. When the query intent is determined to be one of the query intents mapped to a predetermined solution, the predetermined solution for the query may be selected from the predetermined solutions based on the mapping. When the query intent is determined to be a no-solution query intent, the query may be added to a no-solution query set and, when this set accumulates a threshold number of queries, a solution to the problem may be requested from a technician.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include accessing rules that each relate one or more values of the feature vectors to a respective label of a plurality of labels. The actions further include, based on the rules, generating heuristics that each identify related values of the feature vectors. The actions further include, for each of the heuristics, generating a matrix that reflects a similarity of the feature vectors. The actions further include, based on the matrices that each reflects a respective similarity of the feature vectors, generating clusters that each include a subset of the feature vectors. The actions further include, for each cluster, determining a label of the plurality of labels.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include, for a subset of the feature vectors, accessing a first label. The actions further include generating a classifier that is configured to associate a given feature vector with a feature vector of the subset of the feature vectors. The actions further include applying the feature vectors that are not included in the subset of the feature vectors to the classifier. The actions further include generating a dissimilarity matrix. The actions further include, based on the dissimilarity matrix, generating a graph. The actions further include, for each node of the graph, determining a second label. The actions further include, based on the second labels and the first labels, determining a training label for each feature vector.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for facilitating analyzing media items and to filter inappropriate media items before distribution to the users. In one aspect, a method includes partitioning digital media items such as videos into segments and/or scenes, and classifying the segments into predetermined classes such as “Violence”, “Conversation”, “Street”, “Nudity”, “Animation”. After classifications have been assigned, the segments are clustered and/or grouped together before presenting the segments belonging to a particular cluster to a rating entity in a single user interface, for further evaluation. After evaluation, the segments of the media items that were approved by the rating entity are used to identify media items for which all the segments were approved by the rating entity before distributing the media items to the users.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for facilitating analyzing media items and to filter inappropriate media items before distribution to the users. In one aspect, a method includes partitioning digital media items such as videos into segments and/or scenes, and classifying the segments into predetermined classes such as “Violence”, “Conversation”, “Street”, “Nudity”, “Animation”. After classifications have been assigned, the segments are clustered and/or grouped together before presenting the segments belonging to a particular cluster to a rating entity in a single user interface, for further evaluation. After evaluation, the segments of the media items that were approved by the rating entity are used to identify media items for which all the segments were approved by the rating entity before distributing the media items to the users.

Abstract: A device may receive unlabeled data associated with a particular domain and may select sets of data from the unlabeled data. The device may calculate Gaussian kernel densities and minimum distances for data points in each of the sets of data and may calculate anomaly scores for the data points based on the Gaussian kernel densities and the minimum distances for the data points. The device may train a machine learning model, with the anomaly scores for the data points, to generate a trained machine learning model that determines a single anomaly score for the data points, wherein a plurality of single anomaly scores is determined for the sets of data. The device may calculate a final anomaly score for the unlabeled data based on a combination of the plurality of single anomaly scores and may perform one or more actions based on the final anomaly score.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include accessing rules that each relate one or more values of the feature vectors to a respective label of a plurality of labels. The actions further include, based on the rules, generating heuristics that each identify related values of the feature vectors. The actions further include, for each of the heuristics, generating a matrix that reflects a similarity of the feature vectors. The actions further include, based on the matrices that each reflects a respective similarity of the feature vectors, generating clusters that each include a subset of the feature vectors. The actions further include, for each cluster, determining a label of the plurality of labels.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include, for a subset of the feature vectors, accessing a first label. The actions further include generating a classifier that is configured to associate a given feature vector with a feature vector of the subset of the feature vectors. The actions further include applying the feature vectors that are not included in the subset of the feature vectors to the classifier. The actions further include generating a dissimilarity matrix. The actions further include, based on the dissimilarity matrix, generating a graph. The actions further include, for each node of the graph, determining a second label. The actions further include, based on the second labels and the first labels, determining a training label for each feature vector.