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公开(公告)号：US20220414662A1
公开(公告)日：2022-12-29
申请号：US17358575
申请日：2021-06-25
发明人： Shi Cao , Chiranjeet Chetia , Liang Wang , Junpeng Wang , Morvarid Jamalian
摘要： A method for detecting collusive transaction fraud includes: generating a merchant baseline including a transaction data baseline and a time series baseline; extracting time series data of the first merchant system; generating a first score and second score with a deep learning model; generating a first merchant risk score of the first merchant system based on the first and second scores; in response to determining that the first merchant risk score satisfies the threshold, determining a plurality of related entities related to the first merchant system; and classifying the first merchant system and at least one related entity of the plurality of related entities in a first group risk class based on at least one risk score of the at least one related entity.
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2.发明授权公开(公告)号：US11487997B2
公开(公告)日：2022-11-01
申请号：US16593731
申请日：2019-10-04
发明人： Liang Gou , Junpeng Wang , Wei Zhang , Hao Yang
摘要： A method for local approximation of a predictive model may include receiving unclassified data associated with a plurality of unclassified data items. The unclassified data may be classified based on a first predictive model to generate classified data. A first data item may be selected from the classified data. A plurality of generated data items associated with the first data item may be generated using a generative model. The plurality of generated data items may be classified based on the first predictive model to generate classified generated data. A second predictive model may be trained with the classified generated data. A system and computer program product are also disclosed.
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3.发明公开公开(公告)号：US20240289613A1
公开(公告)日：2024-08-29
申请号：US18656024
申请日：2024-05-06
发明人： Haoyu Li , Junpeng Wang , Liang Wang , Yan Zheng , Wei Zhang
IPC分类号： G06N3/08 , G06N3/0455
CPC分类号： G06N3/08 , G06N3/0455
摘要： A method, system, and computer program product is provided for embedding compression and reconstruction. The method includes receiving embedding vector data comprising a plurality of embedding vectors. A beta-variational autoencoder is trained based on the embedding vector data and a loss equation. The method includes determining a respective entropy of a respective mean and a respective variance of each respective dimension of a plurality of dimensions. A first subset of the plurality of dimensions is determined based on the respective entropy of the respective mean and the respective variance for each respective dimension of the plurality of dimensions. A second subset of the plurality of dimensions is discarded based on the respective entropy of the respective mean and the respective variance for each respective dimension of the plurality of dimensions. The method includes generating a compressed representation of the embedding vector data based on the first subset of dimensions.
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公开(公告)号：US20240127035A1
公开(公告)日：2024-04-18
申请号：US18275598
申请日：2022-02-01
发明人： Michael Yeh , Zhongfang Zhuang , Junpeng Wang , Yan Zheng , Javid Ebrahimi , Liang Wang , Wei Zhang
IPC分类号： G06N3/0455
CPC分类号： G06N3/0455
摘要： A method performed by a computer is disclosed. The method comprises receiving interaction data between electronic devices of a plurality of entities. The interaction data is used to form an entity interaction vector containing a number of interactions between the electronic devices of a chosen entity and an entity time series containing a plurality of metrics per unit time of the interactions. An interaction encoder of the computer can generate an interaction hidden representation of the entity interaction vector using embeddings of the plurality of entities. A temporal encoder of the computer can generate a temporal hidden representation of the entity time series. The interaction hidden representation and the temporal hidden representation can be used to generate a predicted scale and a shape estimation of a target interaction metric. The computer can then generate an estimated interaction metric of a time period using the predicted scale and the shape estimation.
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公开(公告)号：US20230186078A1
公开(公告)日：2023-06-15
申请号：US17912070
申请日：2021-04-30
发明人： Junpeng Wang , Wei Zhang , Hao Yang , Michael Yeh , Liang Wang
CPC分类号： G06N3/08 , G06T11/206 , G06Q20/4016 , G06T2200/24
摘要： A method for evaluating a RNN-based deep learning model includes: receiving model data generated by the RNN-based model, the model data including a plurality of events associated with a plurality of states; generating a first GUI based on the events and states including a chart visually representing a timeline for the events in relation to a parameter value; generating a second GUI including a point chart visually representing a two-dimensional projection of the multi-dimensional intermediate data, each point of the point chart representing a time step and an event from the time step, based on multi-dimensional intermediate data between transformations in the model that connect a state to an event; and perturbing the environment at a time step based on user interaction with at least one of the first and second GUIs.
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公开(公告)号：US12118557B2
公开(公告)日：2024-10-15
申请号：US17137524
申请日：2020-12-30
发明人： Liang Wang , Junpeng Wang , Chiranjeet Chetia , Shi Cao , Harishkumar Sundarji Majithiya , Roshni Ann Samuel , Minghua Xu , Wei Zhang , Hao Yang
CPC分类号： G06Q20/4016 , G06F21/552
摘要： Provided is a method for detecting group activities in a network. The method may include receiving interaction data associated with a plurality of interactions. For each account identifier associated with at least one interaction, a value may be determined for each of a first set of categories, and a vector may be generated based on the value for each category. The length of each vector may be determined. At least one relational graph may be generated based on the interaction data. Each relational graph may be associated with a respective category of a second set of categories. At least one cluster of nodes may be determined based on the relational graph(s). A score for each cluster may be determined based on the length of the vector associated with the account identifier of each node of the cluster of nodes. A system and computer program product are also disclosed.
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公开(公告)号：US20240177071A1
公开(公告)日：2024-05-30
申请号：US18281663
申请日：2022-03-30
发明人： Junpeng Wang , Liang Wang , Yan Zheng , Michael Yeh , Shubham Jain , Wei Zhang , Zhongfang Zhuang , Hao Yang
IPC分类号： G06N20/20 , G06F18/2415
CPC分类号： G06N20/20 , G06F18/2415
摘要： Systems, methods, and computer program products may compare machine learning models by identifying data instances with disagreed predictions and learning from the disagreement. Based on a model interpretation technique, differences between the compared machine learning models may be interpreted. Multiple metrics to prioritize meta-features from different perspectives may also be provided.
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8.发明授权公开(公告)号：US11694064B1
公开(公告)日：2023-07-04
申请号：US17953740
申请日：2022-09-27
发明人： Liang Gou , Junpeng Wang , Wei Zhang , Hao Yang
摘要： A method for local approximation of a predictive model may include receiving unclassified data associated with a plurality of unclassified data items. The unclassified data may be classified based on a first predictive model to generate classified data. A first data item may be selected from the classified data. A plurality of generated data items associated with the first data item may be generated using a generative model. The plurality of generated data items may be classified based on the first predictive model to generate classified generated data. A second predictive model may be trained with the classified generated data. A system and computer program product are also disclosed.
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公开(公告)号：US20210224648A1
公开(公告)日：2021-07-22
申请号：US17148984
申请日：2021-01-14
发明人： Zhongfang Zhuang , Michael Yeh , Liang Wang , Wei Zhang , Junpeng Wang
摘要： Described are a system, method, and computer program product for multivariate event prediction using multi-stream recurrent neural networks. The method includes receiving event data from a sample time period and generating feature vectors for each subperiod of each day. The method also includes providing the feature vectors as inputs to a set of first recurrent neural network (RNN) models and generating first outputs for each RNN node. The method further includes merging the first outputs for each same subperiod to form aggregated time-series layers. The method further includes providing the aggregated time-series layers as an input to a second RNN model and generating final outputs for each RNN node of the second RNN model.
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10.发明公开公开(公告)号：US20240256863A1
公开(公告)日：2024-08-01
申请号：US18426717
申请日：2024-01-30
发明人： Huiyuan Chen , Mahashweta Das , Michael Yeh , Yujie Fan , Yan Zheng , Junpeng Wang , Vivian Wan Yin Lai , Hao Yang
摘要： Methods, systems, and computer program products are provided for optimizing training loss of a graph neural network machine learning model using bi-level optimization. An example method includes receiving a training dataset comprising graph data associated with a graph, training a graph neural network (GNN) machine learning model using a loss equation according to a bi-level optimization problem and based on the training dataset, where training the GNN machine learning model using the loss equation according to the bi-level optimization problem includes determining a solution to an inner loss problem and a solution to an outer loss problem, and providing a trained GNN machine learning model based on training the GNN machine learning model.
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