Accenture Global Solutions Limited

Disney Enterprises, Inc.

Nicholas McCarthy

Jeremiah Patrick Hayes

Freddy Lecue

Luca Costabello

Qurrat Ul Ain

Chahrazed Bouhini

Mykhaylo Zayats

Sumit Pai

Christophe Dominique Marie Gueret

Xu Zheng

Rory McGrath

Erika Varis Doggett

Dadong Wan

Stephan Marcel Mandt

Anna M Wolak

Jingguang Han

Jadran Sirotkovic

Penelope Daphne Tsatsoulis

Harshdeep Harshdeep

Patrick Moreau

Christophe Dominique Marie Guéret

Jeremiah Hayes

Philip O'KELLY

Christophe Dominique Gueret

A device processes a knowledge graph to select a set of triples and generates a first class based on the set of triples. The device determines a quantity of quasi-identifier attributes in the first class and compares the quantity to a predefined parameter. The device embeds the knowledge graph to generate an embedding space representation, identifies a second class, and determines a first quantity of nodes in the first class and the second class. The device compares the first quantity to the predefined parameter and identifies a third class. The device determines a second quantity of nodes in the first class and the third class and compares the second quantity to the predefined parameter. The device merges the second class or the third class with the first class, based on the comparisons, to generate anonymized nodes for the knowledge graph.

Applying a k-anonymity model to protect node level privacy in knowledge graphs and a differential privacy model to protect edge level privacy in knowledge graphs

In some implementations, a system may determine, based on a qualification model, a prediction output of an analysis of user information. The system may determine, based on a generator model, a plurality of counterfactual explanations associated with the prediction output and the user information. The system may cluster, according to a clustering model, the plurality of counterfactual explanations into clusters of counterfactual explanations. The system may select, based on a classification model, a counterfactual explanation from a cluster of the clusters of counterfactual explanations. The system may provide a request for feedback associated with the counterfactual explanation. The system may receive feedback data associated with the request for feedback. The system may update a data structure associated with the clustering model based on the feedback data and the counterfactual explanation to form an updated data structure. The system may perform an action associated with the updated data structure.

Generation of counterfactual explanations using artificial intelligence and machine learning techniques

Complex computer system architectures are described for utilizing a knowledge data graph comprised of elements, and selecting a discovery element to replace an existing element of a formulation depicted in the knowledge data graph. The substitution process takes advantage of the knowledge data graph structure to improve the computing capabilities of a computing device executing a substitution calculation by translating the knowledge data graph into an embedding space, and determining a discovery element from within the embedding space.

Compound discovery via information divergence with knowledge graphs

Knowledge graph systems are disclosed for enhancing a knowledge graph by generating a new node. The knowledge graph system converts a knowledge graph into an embedding space, and selects a region of interest from within the embedding space. The knowledge graph system further identifies, from the region of interest, one or more gap regions, and calculates a center for each gap region. A node is generated for each gap region, and the information represented by the node is added to the original knowledge graph to generate an updated knowledge graph.

Density-based computation for information discovery in knowledge graphs

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 for multi-task distribution learning with numeric-aware knowledge graphs

The present disclosure relates to systems, methods, and products for identifying candidate molecule. The system includes a non-transitory memory storing instructions; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to receive drug data; convert the drug data into at least one point in a latent space using a grammar variational auto-encoder (VAE) model; receive a query for the at least one candidate molecule; select one or more points in the latent space; and create a k-dimensional tree graph based on the query for the at least one candidate molecule and the selected one or more points; determine a plurality of paths according to an interpolation technique; receive preference data; determine an optimum path; determine at least one candidate point on the optimum path; and determine a drug molecular structure using an inverse of the grammar VAE model.

Denovo generation of molecules using manifold traversal

A device may generate a synthetic knowledge graph based on a true knowledge graph, may partition the synthetic knowledge graph into a set of synthetic data partitions, and may determine, using a plurality of teacher models, an aggregated prediction. The aggregated prediction may be based on individual predictions from corresponding individual teacher models included in the plurality of teacher models. The device may determine, using a student model and based on the synthetic knowledge graph and noise, a student prediction. The student model may be trained based on historical synthetic knowledge graphs and historical aggregated predictions associated with the plurality of teacher models. The device may determine an error metric based on the aggregated prediction and the student prediction, and may perform an action associated with the synthetic knowledge graph based on the error metric.

Differentially private dataset generation and modeling for knowledge graphs

A process receives, with a processor, video content. Further, the process splices, with the processor, the video content into a plurality of video frames. In addition, the process splices, with the processor, at least one of the plurality of video frames into a plurality of image patches. Moreover, the process performs, with a neural network, an image reconstruction of at least one of the plurality of image patches to generate a reconstructed image patch. The process also compares, with the processor, the reconstructed image patch with the at least one of the plurality of image patches. Finally, the process determines, with the processor, a pixel error within the at least one of the plurality of image patches based on a discrepancy between the reconstructed image patch and the at least one of the plurality of image patches.

Pixel error detection system

The systems and methods described herein may generate multi-modal embeddings with sub-symbolic features and symbolic features. The sub-symbolic embeddings may be generated with computer vision processing. The symbolic features may include mathematical representations of image content, which are enriched with information from background knowledge sources. The system may aggregate the sub-symbolic and symbolic features using aggregation techniques such as concatenation, averaging, summing, and/or maxing. The multi-modal embeddings may be included in a multi-modal embedding model and trained via supervised learning. Once the multi-modal embeddings are trained, the system may generate inferences based on linear algebra operations involving the multi-modal embeddings that are relevant to an inference response to the natural language question and input image.

Multi-modal visual question answering system

A system for providing data security and protection using distributed ledgers to store validated data in a global knowledge graph for fraud detection is disclosed. The system may comprise a data access interface, a processor, and an output interface. The data access interface may receive data associated with an individual from a data source. The processor may convert the data into knowledge graph data by: extracting entities and relations from the data; and translating the data into knowledge graph triples to generate the knowledge graph data. The processor may validate the knowledge graph data using a cryptographic validation, to provide secured contents to update a global knowledge graph to determine a fraudulent activity level associated with the individual based on the updated global knowledge graph. The output interface may transmit a report associated with the fraudulent activity level to a report requestor at a computing device.

Data security and protection system using distributed ledgers to store validated data in a knowledge graph

This disclosure relates to a development and application of a deep-learning neural network (DNN) model for identifying relevance of an information item returned by a search engine in response to a search query by a user, with respect to the search query and a profile for the user. The DNN model includes a set of neural networks arranged to learn correlations between queries, search results, and user profiles using dense numerical word or character embeddings and based on training targets derived from a historical search log containing queries, search results, and user-click data. The DNN model help identifying search results that are relevant to users according to their profiles.

Cognitive searches based on deep-learning neural networks

Implementations include providing a baseline multi-dimensional model of a cultivar, determining an encoding based on the baseline multi-dimensional model, and a target multi-dimensional model, the encoding defining a string of symbols, and being based on an alphabet and a set of rules, providing an expected multi-dimensional model based on the encoding, and a modified set of rules, the modified set of rules being based on the set of rules, the expected multi-dimensional model representing the cultivar after a period of time, selecting a set of actions by determining multiple predicted multi-dimensional models for each set of actions in a plurality of sets of actions, and, for each predicted multi-dimensional model, providing a predicted yield that can be used to determine impact with respect an expected yield, the set of actions being selected based on a respective impact, and providing the set of actions as output for executing on the cultivar.

Modeling and decision support for horticulture

A device may receive individual information associated with individual activities of an individual, and may aggregate the individual information, based on a time period, to generate aggregated individual information. The device may identify patterns in the aggregated individual information, and may determine states for the patterns based on state information associated with activities capable of being performed by individuals. The device may generate a sequential knowledge graph based on modifying a knowledge graph with the states and adding a sequence of activities to the knowledge graph, and may determine embeddings for the individual activities based on the sequential knowledge graph. The device may determine anonymized activity signatures for the individual activities based on the embeddings, and may combine the anonymized activity signatures to generate a time-based anonymized activity signature for the individual, wherein the time-based anonymized activity signature providing information that may be utilized without divulging the individual information.

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Dublin, Ireland

Nicholas McCarthy