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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：US20180357570A1
公开(公告)日：2018-12-13
申请号：US16006208
申请日：2018-06-12
Inventor： Ke SUN , Shiqi ZHAO , Dianhai YU , Haifeng WANG
CPC classification number: G06N99/005 , G06F7/14 , G06F17/2785
Abstract: A method and apparatus for building a conversation understanding system based on artificial intelligence, a device and a computer-readable storage medium. In embodiments of the present disclosure, it is feasible to obtain the training feedback information provided by conversation service conducted by the user and the basic conversation understanding system, then according to the training feedback information, perform adjustment processing for a service state of the basic conversation understanding system, to obtain an adjustment state of the basic conversation understanding system. It is possible to perform data merging processing according to the training feedback information and the adjustment state of the basic conversation understanding system, to obtain model training data for building the model conversation understanding system. This method does not require persons to participate in annotation operations of the training data, exhibits simple operations and a high correctness rate, improving the efficiency and reliability of the conversation understanding system.
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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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5.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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Patent Agency Ranking
公开(公告)号：US20190065507A1
公开(公告)日：2019-02-28
申请号：US16054920
申请日：2018-08-03
Inventor： Shuohuan WANG , Yu SUN , Dianhai YU
Abstract: Embodiments of the present disclosure disclose a method and apparatus for processing information. A specific implementation of the method includes: acquiring a search result set related to a search statement inputted by a user; parsing the search statement to generate a first syntax tree, and parsing a search result in the search result set to generate a second syntax tree set; calculating a similarity between the search statement and the search result in the search result set using a pre-trained semantic matching model on the basis of the first syntax tree and the second syntax tree set, the semantic matching model being used to determine the similarity between the syntax trees; and sorting the search result in the search result set on the basis of the similarity between the search statement and the search result in the search result set, and pushing the sorted search result set to the user.
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公开(公告)号：US20190065506A1
公开(公告)日：2019-02-28
申请号：US16054842
申请日：2018-08-03
Inventor： Yukun LI , Yi LIU , Yu SUN , Dianhai YU
Abstract: Embodiments of the present disclosure disclose a search method and apparatus based on artificial intelligence. A specific implementation of the method comprises: acquiring at least one candidate document related to a query sentence; determining a query word vector sequence corresponding to a segmented word sequence of the query sentence, and determining a candidate document word vector sequence corresponding to a segmented word sequence of each candidate document in the at least one candidate document; performing a similarity calculation for each candidate document in the at least one candidate document; selecting, in a descending order of similarities between the candidate document and the query sentence, a preset number of candidate documents from the at least one candidate document as a search result.
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公开(公告)号：US20180329886A1
公开(公告)日：2018-11-15
申请号：US15900176
申请日：2018-02-20
Inventor： Yukun LI , Yi LIU , Yu SUN , Dianhai YU
CPC classification number: G06F17/2785 , G06F17/277 , G06N3/0454 , G06N3/0481 , G06N3/08
Abstract: An artificial intelligence based method and apparatus for generating information are disclosed. The method in an embodiment includes: segmenting a to-be-processed text into characters to obtain a character sequence; determining a character vector for each character in the character sequence to generate a character vector sequence; generating a plurality of character vector subsequences by segmenting the character vector sequence based on a preset vocabulary; for each generated character vector subsequence, determining a sum of character vectors composing the character vector subsequence as a target vector, and inputting the target vector into a pre-trained first neural network to obtain a word vector corresponding to the each character vector subsequence, the first neural network used to characterize a correspondence between the target vector and the word vector; and analyzing the to-be-processed text based on the obtained word vector to generate an analysis result. This embodiment improves the adaptability of text processing.
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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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