Servicenow Canada Inc.

Element Ai Inc.

Perouz Taslakian

David Vazquez Bermudez

Negin Sokhandan Asl

Guillem Cucurull Preixens

David Poole

Bahare Fatemi

There is provided a method and system for training an embedding model to perform relation predictions in a knowledge hypergraph to output a trained embedding model. A training dataset comprising tuples representing relations between entities in the knowledge hypergraph are received. The embedding model is trained to perform relation predictions for each given tuple from a subset of tuples in the training dataset by generating a respective entity vector for each entity and a respective relation matrix representing relations between the entities. The entity vectors and relation matrix are split into a plurality of windows, and interaction values between elements in each window are calculated. A relation score indicative of the relation in the given tuple being true is calculated. Parameters of the embedding model are updated based on the relation scores for the subset of tuples. The trained embedding model is then output.

Method and system for training model to perform link prediction in knowledge hypergraph

Systems and methods for detecting and predicting text within images. An image is passed to a feature-extraction module. Each image typically contains at least one text object, and each text object contains at least one character. Based on the image, the feature-extraction module generates at least one feature map indicating text object(s) in the image. The feature map(s) is then passed to a decoder module. In son implementations, the decoder module applies a weighted mask to the feature map(s). Based on the feature map(s), the decoder module predicts a sequence of characters in the text object(s). In some embodiments, that prediction is based on previous known data. The decoder module is directed by a query that indicates at least one desired characteristic of the text object(s). An output module then refines the predicted content. At least one neural network may be used.

Automatically predicting text in images

A compatibility score generator implementing a neural network is trained for assessing compatibility of items. Elements of a feature vector representing each item and of a compatibility data structure indicating items considered compatible are retrieved. The neural network is trained using training data corresponding to the items and indicating compatibility between pairs of items. The compatibility data structure is modified by removing indications that items of a pair of items are compatible. An encoding function generating encoded representations for the items based on the compatibility data structure is evaluated. Encoded representations are provided to a decoder that learns a likelihood that the indication had been removed when modified. The neural network and the decoder are optimized based on a loss function that reflects the decoder's ability to correctly determine whether the indication had been removed. The encoded representations generate a compatibility score for at least two items of interest.

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Montreal, Canada

Perouz Taslakian