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公开(公告)号：US20210382699A1
公开(公告)日：2021-12-09
申请号：US17445347
申请日：2021-08-18
Inventor： Shuo TIAN , Tao Luo , Zhefeng Ning , Xiang Lan , Dianhai Yu , Yanjun Ma
Abstract: A data processing method, an electronic device and a computer storage medium, related to the field of artificial intelligence such as deep learning and big data, are provided. The method includes: extracting, in submission information of a current test task, a first sub-directory in which a dependent package required for constructing a model is located; and obtaining the dependent package from a local storage module, in a case where the first sub-directory is as same as a second sub-directory in submission information for a historical test task. Thereby, efficiency of acquiring the dependent package can be increased and execution efficiency of a process for constructing models according to the dependent package is further increased.
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公开(公告)号：US12229667B2
公开(公告)日：2025-02-18
申请号：US17209576
申请日：2021-03-23
Inventor： Daxiang Dong , Wenhui Zhang , Zhihua Wu , Dianhai Yu , Yanjun Ma , Haifeng Wang
IPC: G06N3/08 , G06F9/50 , G06F18/214
Abstract: A method and an apparatus for generating a shared encoder are provided, which belongs to a field of computer technology and deep learning. The method includes: sending by a master node a shared encoder training instruction to child nodes, so that each child node obtains training samples based on a type of a target shared encoder included in the training instruction; sending an initial parameter set of the target shared encoder to be trained to each child node after obtaining a confirmation message returned by each child node; obtaining an updated parameter set of the target shared encoder returned by each child node; determining a target parameter set corresponding to the target shared encoder based on a first preset rule and the updated parameter set of the target shared encoder returned by each child node.
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Patent Agency Ranking
公开(公告)号：US12217150B2
公开(公告)日：2025-02-04
申请号：US17248131
申请日：2021-01-11
Inventor： Huihuang Zheng , Xiang Lan , Yamei Li , Liujie Zhang , Fei Guo , Yanjun Ma , Dianhai Yu
Abstract: A data processing method and apparatus based on a recurrent neural network and a device are provided. The recurrent neural network includes multiple recurrent units, each recurrent unit includes multiple data processing nodes and a start node, at least one recurrent unit includes an end node, and at least one data processing node is included between the start node and the end node. During the processing of the first target processing object in a first recurrent unit, in a case that the first target processing object does not satisfy the first preset condition, the start node in the first recurrent unit is run to add a tag to the data processing nodes subsequent to the start node and stop addition of the tag in response to reaching the end node, and no processing is performed by the data processing nodes with the tag.
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4.发明申请公开(公告)号：US20210326762A1
公开(公告)日：2021-10-21
申请号：US17351194
申请日：2021-06-17
Inventor： Zhihua Wu , Dianhai Yu , Xuefeng Yao , Wei Tang , Xinxuan Wu , Mo Cheng , Lin Ma , Yanjun Ma , Tian Wu , Haifeng Wang
IPC: G06N20/00
Abstract: The present disclosure discloses an apparatus and method for distributedly training a model, an electronic device, and a computer readable storage medium. The apparatus may include: a distributed reader, a distributed trainer and a distributed parameter server that are mutually independent. A reader in the distributed reader is configured to acquire a training sample, and load the acquired training sample to a corresponding trainer in the distributed trainer; the trainer in the distributed trainer is configured to perform model training based on the loaded training sample to obtain gradient information; and a parameter server in the distributed parameter server is configured to update a parameter of an initial model based on the gradient information of the distributed trainer to obtain a trained target model.
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公开(公告)号：US20210216875A1
公开(公告)日：2021-07-15
申请号：US17218033
申请日：2021-03-30
Inventor： Tianjian He , Yi Liu , Daxiang Dong , Yanjun Ma , Dianhai Yu
Abstract: A method for training a deep learning model may include: acquiring model description information and configuration information of a deep learning model; segmenting the model description information into at least two sections based on segmentation point variable in the configuration information, and loading the model description information to a corresponding resource to run; inputting a batch of training samples into a resource corresponding to a first section of model description information, then starting training and using obtained context information as an input of a resource corresponding to a subsequent section of model description information; and so on until an operation result of a resource corresponding to a final section of model description information is obtained; if a training completion condition is met, outputting a trained deep learning model; and otherwise, keeping on acquiring a subsequent batch of training samples and performing the above training steps until the condition is met.
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6.发明授权公开(公告)号：US10902077B2
公开(公告)日：2021-01-26
申请号：US16313195
申请日：2016-09-05
Inventor： Yanjun Ma , Jiachen Liu , Hua Wu
IPC: G06F16/30 , G06F16/9535 , G06F16/953 , G06F16/2458 , G06N20/00 , G06F16/9538
Abstract: The present disclosure provides a search result aggregation method and apparatus based on artificial intelligence and a search engine. The method includes: obtaining a query; generating a plurality of search results according to the query; obtaining a plurality of corresponding demand dimensions according to the query; aggregating the plurality of demand dimensions according to the plurality of search results; obtaining an answer corresponding to each demand dimension, and aggregating the answers corresponding to the plurality of demand dimensions according to the aggregated demand dimensions to generate an aggregation result.
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公开(公告)号：US11954522B2
公开(公告)日：2024-04-09
申请号：US17076346
申请日：2020-10-21
Inventor： Daxiang Dong , Haifeng Wang , Dianhai Yu , Yanjun Ma
CPC classification number: G06F9/4881 , G06F9/52 , G06N20/00
Abstract: Embodiments of the present disclosure disclose a method for processing tasks in parallel, a device and a storage medium, and relate to a field of artificial intelligent technologies. The method includes: determining at least one parallel computing graph of a target task; determining a parallel computing graph and an operator scheduling scheme based on a hardware execution cost of each operator task of each of the at least one parallel computing graph in a cluster, in which the cluster includes a plurality of nodes for executing the plurality of operator tasks, and each parallel computing graph corresponds to at least one operator scheduling scheme; and scheduling and executing the plurality of operator tasks of the determined parallel computing graph in the cluster based on the determined parallel computing graph and the determined operator scheduling scheme.
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