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公开(公告)号:US20220374775A1
公开(公告)日:2022-11-24
申请号:US17867516
申请日:2022-07-18
Inventor: Ji LIU , Beichen MA , Jingbo ZHOU , Ruipu ZHOU , Dejing DOU
Abstract: A method for multi-task scheduling, a device and a storage medium are provided. The method may include: initializing a list of candidate scheduling schemes, the candidate scheduling scheme being used to allocate a terminal device for training to each machine learning task in a plurality of machine learning tasks; perturbing, for each candidate scheduling scheme in the list of candidate scheduling schemes, the candidate scheduling scheme to generate a new scheduling scheme; determining whether to replace the candidate scheduling scheme with the new scheduling scheme based on a fitness value of the candidate scheduling scheme and a fitness value of the new scheduling scheme, to generate a new scheduling scheme list; and determining a target scheduling scheme, based on the fitness value of each new scheduling scheme in the new scheduling scheme list.
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公开(公告)号:US20220237474A1
公开(公告)日:2022-07-28
申请号:US17721659
申请日:2022-04-15
Inventor: Yanyan LI , Jingbo ZHOU , Jizhou HUANG , Dejing DOU
IPC: G06N5/02 , G06F16/901
Abstract: A method and apparatus for semanticization is provided. The method includes: ascertaining a target coordinate of a to-be-semanticized location; ascertaining, through a pre-built regional spatial index tree, a target region to which the target coordinate of the to-be-semanticized location belongs; and ascertaining semantic information of the to-be-semanticized location based on semantic information of the target region.
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13.
公开(公告)号:US20250103589A1
公开(公告)日:2025-03-27
申请号:US18974155
申请日:2024-12-09
Inventor: Yixiong XIAO , Jingbo ZHOU
IPC: G06F16/242
Abstract: Data query method and apparatus based on large model, an electronic device, and a storage medium are disclosed, which relates to the field of artificial intelligence, specifically in natural language processing, deep learning, and large model technologies, applicable to scenarios such as dialogue systems and information retrieval. The method includes: performing entity recognition on a query to obtain the target entity in the query; obtaining a first related content associated with the target entity from internal information, and performing data analysis on the first related content using a large language model (LLM) to obtain a data analysis result; obtaining a second related content associated with the target entity from external information, and performing data generation on the second related content using the LLM to obtain a data generation result; obtaining a query result corresponding to the query based on the data analysis result and the data generation result.
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公开(公告)号:US20250014766A1
公开(公告)日:2025-01-09
申请号:US18895554
申请日:2024-09-25
Inventor: Jingbo ZHOU , Yuhan YE
Abstract: A drug reaction prediction method, which is related to the field of artificial intelligence, specifically involving deep learning, computational biology, and chemistry, is disclosed. The drug reaction prediction method includes: obtaining a target graph based on multiple levels of entities contained in a drug to be predicted; the target graph includes an entity graph representing topological information within the entities and an interaction graph representing correlation information between the entities; performing representation extraction processing on the target graph to obtain an initial representation; obtaining a target representation based on a predetermined prompt identifier and the initial representation; and obtaining a drug reaction prediction result for the drug to be predicted based on the target representation.
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公开(公告)号:US20240282103A1
公开(公告)日:2024-08-22
申请号:US18654477
申请日:2024-05-03
Inventor: Congxi XIAO , Jizhou HUANG , Jingbo ZHOU
CPC classification number: G06V20/176 , G06V10/26 , G06V10/761 , G06V10/82
Abstract: A data updating method, a model training method and related devices are provided. The method includes obtaining urban graph data in a preset region, the urban graph data including a node set including central nodes, an edge set and a feature set, the edge set including neighborhoods corresponding to the central nodes, the neighborhoods including other nodes possessing connecting edges with the central nodes, the neighborhoods corresponding to a target region, and the feature set including node features of the nodes in the node set; partitioning the target region into at least two sub-regions to obtain a region partition set; aggregating the node features corresponding to all nodes located within the same sub-region to obtain the regional features of each of the sub-regions; updating the node features of the central node based on the regional features of the sub-regions in the region partition set to obtain target feature data.
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16.
公开(公告)号:US20230013055A1
公开(公告)日:2023-01-19
申请号:US17945979
申请日:2022-09-15
Inventor: Yanyan LI , Jingbo ZHOU , Jizhou HUANG , Airong JIANG , Dejing DOU
Abstract: A method is provided. The method includes: determining, by one or more computers, a name of a target region, wherein the name of the target region is determined based on geometry attribute information of the target region; and determining, by one or more computers, region attribute information of the target region based on the name of the target region
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