An apparatus including circuitry configured to determine a probability by combining at least: a probability that an event is present within a current feature of interest given a first set of previous features of interest, and a probability that the event is present within the current feature of interest given a second set of previous features of interest, different to the first set of previous features of interest; circuitry configured to detect the event based on the determined probability; and circuitry configured to control, in dependence on the detection of the event, performance of an action.

The present disclosure describes methods and systems to predict predicate metadata parameters in knowledge graphs via neural networks. The method includes receiving a knowledge graph based on a knowledge base including a graph-based dataset. The knowledge graph includes a predicate between two nodes and a set of predicate metadata. The method also includes determining a positive structural score, adjusting each positive structural score based on each corresponding significance parameter, generating a synthetic negative graph-based dataset, determining a negative structural score for each synthetic negative triple of the synthetic negative graph-based dataset, adjusting each negative structural score based on each corresponding significance parameter, determining a significance loss value based on the adjusted positive structural scores and the adjusted negative structural scores, and determining a likelihood score of a link between a third node and a fourth node in the knowledge graph based on the significance loss value.

This disclosure provides methods and systems for predicting missing links and previously unknown numerals in a knowledge graph. A jointly trained multi-task machine learning model is disclosed for integrating a symbolic pipeline for predicting missing links and a regression numerical pipeline for predicting numerals with prediction uncertainty. The two prediction pipelines share a jointly trained embedding space of entities and relationships of the knowledge graph. The numerical pipeline additionally includes a second-layer multi-task regression neural network containing multiple regression neural networks for parallel numerical prediction tasks with a cross stich network allowing for information/model parameter sharing between the various parallel numerical prediction tasks.

System, method and architecture for providing improved visual recognition by modeling visual content, semantic content and an implicit social network representing individuals depicted in a collection of content, such as visual images, photographs, etc., which network may be determined based on co-occurrences of individuals represented by the content, and/or other data linking the individuals. In accordance with one or more embodiments, using images as an example, a relationship structure may comprise an implicit structure, or network, determined from co-occurrences of individuals in the images. A kernel jointly modeling content, semantic and social network information may be built and used in automatic image annotation and/or determination of relationships between individuals, for example.

A method for partially occluded object detection includes obtaining a response map for a detection window of an input image, the response map based on a trained model and including a root layer and a parts layer. The method includes determining visibility flags for each root cell of the root layer and each part of the parts layer. The visibility flag is one of visible or occluded. The method includes determining an occlusion penalty for each root cell with a visibility flag of occluded and for each part with a visibility flag of occluded. The occlusion penalty is based on a location of the root cell or the part with respect to the detection window. The method determines a detection score for the detection window based on the visibility flags and the occlusion penalties and generates an estimated visibility map for object detection based on the detection score.

A system maintains a knowledge layout to support the building of event response recommendations. Meta-graph patterns may be used to determine semantic relatedness between events and actions in response. Event-action node pairs are then constructed.

One or more embodiments of the present disclosure include systems and methods that use neural architecture fusion to learn how to combine multiple separate pre-trained networks by fusing their architectures into a single network for better computational efficiency and higher accuracy. For example, a computer implemented method of the disclosure includes obtaining multiple trained networks. Each of the trained networks may be associated with a respective task and has a respective architecture. The method further includes generating a directed acyclic graph that represents at least a partial union of the architectures of the trained networks. The method additionally includes defining a joint objective for the directed acyclic graph that combines a performance term and a distillation term. The method also includes optimizing the joint objective over the directed acyclic graph.

A graph model of a graphical user interface (GUI) may be generated by processing usage data of the GUI where the usage data comprises sequences of GUI pages and actions between GUI pages. The nodes of the graph model may be determined by obtaining GUI pages from the usage data, identifying dynamic GUI elements in the GUI pages, generating canonical GUI pages by modifying the GUI pages using the dynamic GUI elements, and creating graph nodes using the canonical GUI pages. The edges of the graph may be determined by processing actions from the GUI data that were performed by users to transition from one GUI page to another GUI page. The graph model of the GUI may be used for any appropriate application, such as determining statistics relating to the GUI or statistics relating to individual users of the GUI.

A method of generating a quantized neural network comprises (i) receiving a pre-trained neural network model and (ii) modifying the pre-trained neural network model to calculate one or more statistics on an output of one or more layers of the pre-trained neural network model based on a current image and set up an output data format for one or more following layers of the pre-trained neural network model for one or more of the current image and a subsequent image dynamically based on the one or more statistics.

A method for determining negative samples for training a knowledge graph embedding of a knowledge graph enhanced by an ontology including at least one constraint for distinguishing a fact of the knowledge graph from a spurious fact. The method comprises determining embedding predicted triples; determining a set of triples that comprises a triple of the knowledge graph and at least one of the predicted triples that are inconsistent with respect to the ontology; determining from the set of triples a replacement entity for the object entity in the at least one triple of the predicted triples; and determining the negative sample to comprise the relation, the subject entity and the replacement entity, or determining from the subset a replacement entity for the subject entity in the at least one triple of the predicted triples and determining the negative sample to comprise the relation, the object entity, and the replacement entity.