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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.
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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.
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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.
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公开(公告)号：US20210390393A1
公开(公告)日：2021-12-16
申请号：US17128978
申请日：2020-12-21
Inventor： Zhengjie HUANG , Weibin LI , Shikun FENG
Abstract: A method for pre-training a graph neural network, an electronic device and a readable storage medium, which relate to the technical field of deep learning are proposed. An embodiment for pre-training a graph neural network includes: acquiring an original sample to be used for training; expanding the original sample to obtain a positive sample and a negative sample corresponding to the \original sample; constructing a sample set Corresponding to the original sample by using the original sample and the positive sample, the negative sample, and a weak sample corresponding to the original sample; and pre-training the graph neural network by taking the original sample and one of other samples in the sample set as input of the graph neural network respectively, until the graph neural network converges. The technical solution may implement pre-training of a graph neural network at a graph level.
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