Abstract: A method for predicting one or more events includes generating, for features of each of at least two feature types, an intermediate representation using a representation learning model for the at least two feature types. The intermediate representations of the at least two feature types are analyzed using a neural network and at least one neural network model so as to provide a joint representation for predicting certain events. One or more actions to be taken can be determined based on the one or more events predicted by the joint representation.

Abstract: A method for predicting a patient outcome from a caretaker episode includes receiving a current episode snapshot of the caretaker episode comprising multi-modal data of the patient from an electronic health records (EHR) system, the multi-modal data including one or more available data modalities and one or more missing data modalities. The multi-modal data is applied as input to an embedding model having a submodel for each of the data modalities. A first embedding is generated for each of the available data modalities. A second embedding is generated for each of the missing data modalities using corresponding embeddings of neighbors in an episode snapshot graph. The first and second embeddings are combined to obtain a complete embedding. The patient outcome is predicted based on the complete embedding for the current episode snapshot using a machine learning component which has been trained using patient outcomes of the historical episode snapshots.

Abstract: A method for learning latent representations of individual users in a personalization system uses a graph-based machine learning framework. A graph representation is generated based on input data in which the individual users are each represented by a node. The nodes are associated with labels. Node vector representations are learned by combining label latent representations from a vertex and neighboring nodes so as to reconstruct the label latent representation of the vertex and updating the label latent representations of the neighboring nodes using gradients resulting from application of a reconstruction loss. A classifier/regressor is trained using the node vector representations and the node vector representations are mapped to personalizations. Actions associated with the personalizations are then initiated.

Abstract: A method for providing recommendations to users includes obtaining stored data structure triples and actual ratings associated with the data structure triples; training a machine learning model using the stored data structure triples and associated actual ratings, wherein training the machine learning model includes generating user, product, and review representations based on the stored data structure triples and their associated ratings; predicting, by the machine learning model, ratings using the generated user, product, and review representations; and making recommendations based on the predicted ratings.

Abstract: A method for assigning a server to provide a resource to a client in a distributed network includes receiving a request for the resource from the client. A network metric is measured at different points in the network. The network metric measurements are input to a deep learning model. Using the model, the network metric is predicted between the client and each of a plurality candidate servers which have the resource and have not had a prior connection with the client. One of the candidate servers is assigned to provide the resource to the client based on the predictions of the network metric.

Abstract: A method for learning numerical attributes in a knowledge graph includes learning knowledge graph embeddings based on jointly minimizing a knowledge graph loss and a number of numerical attribute prediction losses. The method also includes executing a numerical attribute propagation algorithm using an adjacency matrix of the knowledge graph and numerical values of labeled nodes of the knowledge graph to predict missing ones of the numerical attributes.

Abstract: A method of incorporating temporal information into a knowledge graph comprising triples in a form of subject, predicate and object for link prediction, includes the step of determining, for each of the triples, a predicate sequence including a concatenation of a predicate token and, for the triples having the temporal information available, a sequence of temporal tokens, the predicate tokens including at least a relation type token. The predicate sequences are input to a recursive neural network so as to learn representations of the predicate sequences which carry the temporal information. The learned representations of the predicate sequences are used along with embeddings of the subjects and objects in a scoring function for the link prediction.

Abstract: A method for predicting one or more events includes generating, for features of each of at least two feature types, an intermediate representation using a representation learning model for the at least two feature types. The intermediate representations of the at least two feature types are analyzed using a neural network and at least one neural network model so as to provide a joint representation for predicting certain events. One or more actions to be taken can be determined based on the one or more events predicted by the joint representation.

Abstract: A method for predicting a patient outcome from a caretaker episode includes receiving a current episode snapshot of the caretaker episode comprising multi-modal data of the patient from an electronic health records (EHR) system, the multi-modal data including one or more available data modalities and one or more missing data modalities. The multi-modal data is applied as input to an embedding model having a submodel for each of the data modalities. A first embedding is generated for each of the available data modalities. A second embedding is generated for each of the missing data modalities using corresponding embeddings of neighbors in an episode snapshot graph. The first and second embeddings are combined to obtain a complete embedding. The patient outcome is predicted based on the complete embedding for the current episode snapshot using a machine learning component which has been trained using patient outcomes of the historical episode snapshots.

Abstract: A method is used to learn classifier-agnostic node representations that are independent from particular classification functions and carry class label information. The method includes learning representations of nodes of a graph structure according to an unsupervised learning framework by applying a distance-based or similarity-based loss between the nodes. Embeddings of the class label information are learned for at least some of the nodes. The learned embeddings of the class label information are injected into the node representations learned according to the unsupervised learning framework.

Abstract: A method for assigning a server to provide a resource to a client in a distributed network includes receiving a request for the resource from the client. A network metric is measured at different points in the network. The network metric measurements are input to a deep learning model. Using the model, the network metric is predicted between the client and each of a plurality candidate servers which have the resource and have not had a prior connection with the client. One of the candidate servers is assigned to provide the resource to the client based on the predictions of the network metric.

Abstract: A method for providing recommendations to users includes obtaining stored data structure triples and actual ratings associated with the data structure triples; training a machine learning model using the stored data structure triples and associated actual ratings, wherein training the machine learning model includes generating user, product, and review representations based on the stored data structure triples and their associated ratings; predicting, by the machine learning model, ratings using the generated user, product, and review representations; and making recommendations based on the predicted ratings.

Abstract: A method for learning latent representations of individual users in a personalization system uses a graph-based machine learning framework. A graph representation is generated based on input data in which the individual users are each represented by a node. The nodes are associated with labels. Node vector representations are learned using message passing. A classifier/regressor is trained using the node vector representations and mapping the node vector representations are mapped to personalizations. Actions associated with the personalizations are then initiated.