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公开(公告)号：US20200068807A1
公开(公告)日：2020-03-05
申请号：US16117153
申请日：2018-08-30
摘要： 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.
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公开(公告)号：US11593666B2
公开(公告)日：2023-02-28
申请号：US16899365
申请日：2020-06-11
发明人： Nicholas McCarthy , Sumit Pai , Luca Costabello
摘要： 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.
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公开(公告)号：US11475161B2
公开(公告)日：2022-10-18
申请号：US16887927
申请日：2020-05-29
发明人： Xu Zheng , Nicholas McCarthy , Jeremiah Hayes
摘要： 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.
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公开(公告)号：US20210264110A1
公开(公告)日：2021-08-26
申请号：US16884174
申请日：2020-05-27
摘要： 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.
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公开(公告)号：US11789991B2
公开(公告)日：2023-10-17
申请号：US16256424
申请日：2019-01-24
发明人： Freddy Lecue , Chahrazed Bouhini , Jeremiah Hayes , Mykhaylo Zayats , Nicholas McCarthy , Qurrat Ul Ain
IPC分类号： G06N5/02 , G06F16/36 , G06F17/11 , G06F16/2457 , G06F18/22 , G06F18/213
CPC分类号： G06F16/36 , G06F16/2457 , G06F17/11 , G06F18/213 , G06F18/22 , G06N5/02
摘要： 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.
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公开(公告)号：US20210216881A1
公开(公告)日：2021-07-15
申请号：US16899365
申请日：2020-06-11
发明人： Nicholas McCarthy , Sumit Pai , Luca Costabello
摘要： 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.
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公开(公告)号：US20200242484A1
公开(公告)日：2020-07-30
申请号：US16256424
申请日：2019-01-24
发明人： Freddy Lecue , Chahrazed Bouhini , Jeremiah Hayes , Mykhaylo Zayats , Nicholas McCarthy , Qurrat Ul Ain
IPC分类号： G06N5/02 , G06K9/62 , G06F16/2457 , G06F17/11
摘要： 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.
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公开(公告)号：US10949718B2
公开(公告)日：2021-03-16
申请号：US16406380
申请日：2019-05-08
发明人： Luca Costabello , Nicholas McCarthy , Rory McGrath , Sumit Pai
IPC分类号： G06K9/72 , G06F3/0484 , G06K9/62 , G06F40/30
摘要： 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.
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公开(公告)号：US10803055B2
公开(公告)日：2020-10-13
申请号：US15843892
申请日：2017-12-15
IPC分类号： G06F16/242 , G06N3/08 , G06F16/31
摘要： 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.
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公开(公告)号：US20190188295A1
公开(公告)日：2019-06-20
申请号：US15843892
申请日：2017-12-15
摘要： 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.
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