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公开(公告)号：US20210406641A1
公开(公告)日：2021-12-30
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
IPC: G06N3/04
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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公开(公告)号：US20210255896A1
公开(公告)日：2021-08-19
申请号：US17076346
申请日：2020-10-21
Inventor： Daxiang DONG , Haifeng WANG , Dianhai YU , Yanjun MA
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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3.发明申请公开(公告)号：US20210357814A1
公开(公告)日：2021-11-18
申请号：US17362674
申请日：2021-06-29
Inventor： Xinxuan WU , Xuefeng YAO , Dianhai YU , Zhihua WU , Yanjun MA , Tian WU , Haifeng WANG
IPC: G06N20/00
Abstract: The present disclosure provides a method and apparatus for distributed training a model, an electronic device, and a computer readable storage medium. The method may include: performing, for each batch of training samples acquired by a distributed first trainer, model training through a distributed second trainer to obtain gradient information; updating a target parameter in a distributed built-in parameter server according to the gradient information; and performing, in response to determining that training for a preset number of training samples is completed, a parameter exchange between the distributed built-in parameter server and a distributed parameter server through the distributed first trainer to perform a parameter update on the initial model until training for the initial model is completed.
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Patent Agency Ranking
公开(公告)号：US20210406767A1
公开(公告)日：2021-12-30
申请号：US17142822
申请日：2021-01-06
Inventor： Daxiang DONG , Weibao GONG , Yi LIU , Dianhai YU , Yanjun MA , Haifeng WANG
IPC: G06N20/00 , G06F16/182 , G06N5/04
Abstract: The present application discloses a distributed training method and system, a device and a storage medium, and relates to technical fields of deep learning and cloud computing. The method includes: sending, by a task information server, a first training request and information of an available first computing server to at least a first data server; sending, by the first data server, a first batch of training data to the first computing server, according to the first training request; performing, by the first computing server, model training according to the first batch of training data, sending model parameters to the first data server so as to be stored after the training is completed, and sending identification information of the first batch of training data to the task information server so as to be recorded; wherein the model parameters are not stored at any one of the computing servers.
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公开(公告)号：US20210209417A1
公开(公告)日：2021-07-08
申请号：US17209576
申请日：2021-03-23
Inventor： Daxiang DONG , Wenhui ZHANG , Zhihua WU , Dianhai YU , Yanjun MA , Haifeng WANG
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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公开(公告)号：US20220114218A1
公开(公告)日：2022-04-14
申请号：US17279377
申请日：2020-06-09
Inventor： Tianjian HE , Yi LIU , Daxiang DONG , Yanjun MA , Dianhai YU
IPC: G06F16/901 , G06F9/30 , G06N3/08
Abstract: A session recommendation method, a device and an electronic device are provided, related to the field of graph neural network technology. The session recommendation method includes: acquiring a session control sequence, and acquiring a first embedding vector matrix based on an embedding vector of each of items in the session control sequence; generating a position information sequence based on an arrangement sequence of the items in the session control sequence, and acquiring a second embedding vector matrix based on an embedding vector of each piece of position information in the position information sequence; determining a target embedding vector matrix based on the first embedding vector matrix and the second embedding vector matrix; and determining a recommended item, based on the target embedding vector matrix and through a Session-based Recommendation Graph Neural Network.
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7.发明申请公开(公告)号：US20210374356A1
公开(公告)日：2021-12-02
申请号：US17399016
申请日：2021-08-10
Inventor： Tianjian HE , Yi LIU , Daxiang DONG , Dianhai YU , Yanjun MA
Abstract: The disclosure discloses a conversation-based recommending method. A directed graph corresponding to a current conversation is obtained. The current conversation includes clicked items, the directed graph includes nodes and directed edges between the nodes, each node corresponds to a clicked item, and each directed edge indicates relationship data between the nodes. For each node of the directed graph, an attention weight is determined for each directed edge corresponding to the node based on a feature vector of the node and the relationship data for each node of the directed graph. A new feature vector of the node is determined based on the relationship data and the attention weight of each directed edge. A feature vector of the current conversation is determined based on the new feature vector of each node. An item is recommended based on the feature vector of the current conversation.
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8.发明申请公开(公告)号：US20190163714A1
公开(公告)日：2019-05-30
申请号：US16313195
申请日：2016-09-05
Inventor： Yanjun MA , Jiachen LIU , Hua WU
IPC: G06F16/9535 , G06F16/2458 , G06F16/9538 , G06N20/00
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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