-
公开(公告)号：US20210397947A1
公开(公告)日：2021-12-23
申请号：US17116291
申请日：2020-12-09
Inventor： Weibin LI , Zhifan ZHU , Shikun FENG , Jingzhou HE , Shiwei HUANG
Abstract: Embodiments of the present disclosure provide a method for generating a model for representing heterogeneous graph node. A specific implementation includes: acquiring a training data set, wherein the training data set includes node walk path information obtained by sampling a heterogeneous graph according to different meta paths; and training, based on a gradient descent algorithm, an initial heterogeneous graph node representation model with the training data set as an input of the initial heterogeneous graph node representation model, to obtain a heterogeneous graph node representation model.
-
公开(公告)号：US20210201198A1
公开(公告)日：2021-07-01
申请号：US16945183
申请日：2020-07-31
Inventor： Weibin LI , Zhifan ZHU , Weiyue SU , Jingzhou HE , Shikun FENG , Yuhui CAO , Xuyi CHEN , Danxiang ZHU
IPC: G06N20/00 , G06F16/901
Abstract: A method for generating node representations in a heterogeneous graph, an electronic device, and a non-transitory computer-readable storage medium, and relates to the field of machine learning technologies. The method includes: acquiring a heterogeneous graph; inputting the heterogeneous graph into a heterogeneous graph learning model to generate a node representation of each node in the heterogeneous graph, in which the heterogeneous graph learning model generates the node representation of each node by actions of: segmenting the heterogeneous graph into a plurality of subgraphs, in which each subgraph includes nodes of two types and an edge of one type between the nodes of two types; and generating the node representation of each node according to the plurality of subgraphs.
-
公开(公告)号：US20210383233A1
公开(公告)日：2021-12-09
申请号：US17101748
申请日：2020-11-23
Inventor： Weiyue SU , Shikun FENG , Zhifan ZHU , Weibin LI , Jingzhou HE , Shiwei HUANG
Abstract: The disclosure discloses a method for distilling a model, an electronic device, and a storage medium, and relates to the field of deep learning technologies. A teacher model and a student model are obtained. The second intermediate fully connected layer is transformed into an enlarged fully connected layer and a reduced fully connected layer based on a first data processing capacity of a first intermediate fully connected layer of the teacher model and a second data processing capacity of a second intermediate fully connected layer of the student model. The second intermediate fully connected layer is replaced with the enlarged fully connected layer and the reduced fully connected layer to generate a training student model. The training student model is distilled based on the teacher model.
-
公开(公告)号：US20180349350A1
公开(公告)日：2018-12-06
申请号：US15921386
申请日：2018-03-14
Inventor： Zhifan ZHU , Shikun FENG , Kunsheng ZHOU , Jingzhou HE
Abstract: This disclosure discloses an artificial intelligence based method and apparatus for checking a text. An embodiment of the method comprises: lexing a first to-be-checked text and a second to-be-checked text respectively, determining word vectors of the lexed words to generate a first word vector sequence and a second word vector sequence; inputting the first word vector sequence and the second word vector sequence respectively into a pre-trained convolutional neural network containing at least one multi-scale convolutional layer, identifying vector sequences in a plurality of vector sequences outputted by a last multi-scale convolutional layer as eigenvector sequences, to obtain eigenvector sequence groups respectively corresponding to the texts; combining eigenvector sequences in each eigenvector sequence group to generate a combined eigenvector sequence; and analyzing the generated combined eigenvector sequences to determine whether the first text and the second text pass a similarity check. The embodiment improves the flexibility in checking a text.
-
-
-
Search Results
4
records
(took 0.02 seconds)
