Patents by Inventor Jiankai SUN
Jiankai SUN has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11984666Abstract: An object of the present disclosure is to provide a radiation element and a bandwidth extension structure. The radiation element according to the present disclosure comprises: a basic radiation element and one or more bandwidth extension structures; wherein the one or more bandwidth extension structures are mounted on the basic radiation element to extend the operating bandwidth of the basic radiation element. The bandwidth extension structure according to the present disclosure is mounted on the basic radiation element to extend the operating band of the basic radiation element.Type: GrantFiled: November 2, 2018Date of Patent: May 14, 2024Assignee: RFS Technologies, Inc.Inventors: Jiankai Xu, Ke Chen, Chunhua Zhou, Jing Liu, Jihong Sun
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Publication number: 20240126899Abstract: There are proposed a method, device, apparatus, and medium for protecting sensitive data. In a method, to-be-processed data is received from a server device. A processing result of a user for the to-be-processed data is received, the processing result comprising sensitive data of the user for the processing of the to-be-processed data. A gradient for training a server model at the server device is determined based on a comparison between the processing result and a prediction result for the to-be-processed data. The gradient is updated in a change direction associated with the gradient so as to generate an updated gradient to be sent to the server device. Noise is added only in the change direction associated with the gradient. The corresponding overhead of processing noise in a plurality of directions can be reduced, and no excessive noise data interfering with training will be introduced to the updated gradient.Type: ApplicationFiled: December 14, 2023Publication date: April 18, 2024Inventors: Xin YANG, Junyuan XIE, Jiankai SUN, Yuanshun YAO, Chong WANG
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Publication number: 20240119341Abstract: The present disclosure describes techniques for determining performance of a classifier. A first machine learning model and a second machine learning model may be trained by aggregating updates to the first machine learning model and the second machine learning model received from a plurality of client computing devices. A cumulative distribution function (CDF) associated with a distribution of the positive samples in the user data may be estimated using the trained first machine learning model. A probability density function (PDF) associated with a distribution of the negative samples in the user data may be estimated using the trained second machine learning model. An integration-based computation of an area under the receiver operating characteristic curve (AUC) of the classifier may be performed using the PDF and the CDF.Type: ApplicationFiled: September 26, 2022Publication date: April 11, 2024Inventors: Xin YANG, Hanlin ZHU, Tianyi LIU, Jiankai SUN, Yuanshun YAO, Aonan ZHANG, Chong WANG
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Publication number: 20240070525Abstract: The present disclosure describes techniques of performing machine unlearning in a recommendation model. An unlearning process of the recommendation model may be initiated in response to receiving a request for deleting a fraction of user data from any particular user. The recommendation model may be pre-trained to recommend content to users based at least in part on user data. Values of entries in a matrix corresponding to the fraction of user data may be configured as zero. The matrix may comprise entries denoting preferences of users with respect to content items. Confidence values associated with the fraction of user data may be configured as zero to block influence of the fraction of user data on performance of the recommendation model. The unlearning process may be implemented by performing a number of iterations until the recommendation model has converged.Type: ApplicationFiled: August 29, 2022Publication date: February 29, 2024Inventors: Jiankai Sun, Xinlei Xu, Xin Yang, Yuanshun Yao, Chong Wang
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Publication number: 20240005210Abstract: The present disclosure relates to a data protection method, an apparatus, a medium and a device. The method includes: acquiring gradient association information respectively corresponding to reference samples of a target batch of an active party of a joint training model; according to the proportion occupied respectively by reference samples of positive examples and reference samples of negative examples in all reference samples of the target batch, determining a constraint condition of the data noise to be added; determining information of said data noise according to the gradient association information and the constraint condition corresponding to the reference samples; correcting, according to the information of said data noise, an initial gradient transmission value corresponding to each reference sample, so as to obtain target gradient transmission information; and sending the target gradient transmission information to a passive party of the joint training model.Type: ApplicationFiled: November 6, 2021Publication date: January 4, 2024Inventors: Jiankai SUN, Weihao GAO, Chong WANG, Hongyi ZHANG, Xiaobing LIU, Runliang LI, Xin YANG
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Patent number: 11763204Abstract: Disclosed in the embodiments of the present invention are a method and an apparatus for training an item coding model. The method comprises: acquiring an initial item coding model and a training sample set; using sample user information of training samples in the training sample set as the input for the initial item coding model to obtain the probability of sample item coding information corresponding to the inputted sample user information; adjusting the structural parameters of the initial item coding model to train an item coding model, the item coding model being used for characterizing the correspondence between inputted sample user information and sample item coding information and the correspondence between sample item information and sample item coding information. The present embodiment can use the trained item coding model to implement item recommendation and can use the item coding information as an index to increase retrieval efficiency.Type: GrantFiled: July 29, 2022Date of Patent: September 19, 2023Assignees: BEIJING BYTEDANCE NETWORK TECHNOLOGY CO., LTD., BYTEDANCE INC.Inventors: Weihao Gao, Xiangjun Fan, Jiankai Sun, Wenzhi Xiao, Chong Wang, Xiaobing Liu
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Patent number: 11755691Abstract: Disclosed are a data protection method and apparatus, and a server and a medium. A particular embodiment of the method comprises: acquiring gradient associated information, which respectively corresponds to a target sample that belongs to a binary classification sample set with unbalanced distribution and a reference sample that belongs to the same batch as the target sample; generating information of data noise to be added; according to the information of said data noise, correcting an initial gradient transfer value corresponding to the target sample, such that corrected gradient transfer information corresponding to samples in the sample set that belong to different types is consistent; and sending the gradient transfer information to a passive party of a joint training model. By means of the embodiment, there is no significant difference between corrected gradient transfer information corresponding to positive and negative samples, thereby effectively protecting the security of data.Type: GrantFiled: July 29, 2022Date of Patent: September 12, 2023Assignees: BEIJING BYTEDANCE NETWORK TECHNOLOGY CO., LTD., BYTEDANCE INC.Inventors: Jiankai Sun, Weihao Gao, Hongyi Zhang, Chong Wang, Junyuan Xie, Liangchao Wu, Xiaobing Liu
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Publication number: 20230143789Abstract: Split learning is provided to train a composite neural network (CNN) model that is split into first and second submodels, including receiving a noise-laden backpropagation gradient, training the surrogate submodel by optimizing a gradient distance loss, and computing an updated dummy label using the first submodel and the trained surrogate submodel to infer label information of the second submodel. Noise can be added to a label of the second submodel or a shared backpropagation gradient to protect the label information.Type: ApplicationFiled: January 3, 2023Publication date: May 11, 2023Inventors: Shangyu Xie, Jiankai Sun, Xin Yang, Yuanshun Yao, Tianyi Liu, Taiqing Wang
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Publication number: 20230106448Abstract: The present disclosure describes techniques for diversifying recommendations by improving embedding generation of a Graph Neural Network (GNN) model. A subset of neighbors for each GNN item node may be selected on an embedding space for aggregation. The subset of neighbors may comprise diverse items and may represent an entire set of neighbors of the GNN item node. Attention weights may be assigned for a plurality of layers of the GNN model to mitigate over-smoothing of the GNN model. Loss reweighting may be performed by adjusting weight for each sample item during training the GNN model based on a category of the sample item to focus on learning of long-tail categories.Type: ApplicationFiled: November 11, 2022Publication date: April 6, 2023Inventors: Liangwei YANG, Shengjie WANG, Yunzhe TAO, Jiankai SUN, Taiqing WANG
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Publication number: 20230098656Abstract: The present disclosure describes techniques for improving data subsampling for recommendation systems. A user-item graph associated with training data may be constructed. An importance of user-item interactions may be estimated via graph conductance based on the user-item graph. An importance of the training data may be measured via sample hardness using a pre-trained pilot model. A subsampling rate may be generated based on the importance estimated from the user-item graph and the importance measured by the pre-trained pilot model.Type: ApplicationFiled: November 28, 2022Publication date: March 30, 2023Inventors: Aonan Zhang, Jiankai Sun, Ruocheng Guo, Taiqing Wang, Xiaohui Chen
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Publication number: 20220383054Abstract: Disclosed are a data protection method and apparatus, and a server and a medium. A particular embodiment of the method comprises: acquiring gradient associated information, which respectively corresponds to a target sample that belongs to a binary classification sample set with unbalanced distribution and a reference sample that belongs to the same batch as the target sample; generating information of data noise to be added; according to the information of said data noise, correcting an initial gradient transfer value corresponding to the target sample, such that corrected gradient transfer information corresponding to samples in the sample set that belong to different types is consistent; and sending the gradient transfer information to a passive party of a joint training model. By means of the embodiment, there is no significant difference between corrected gradient transfer information corresponding to positive and negative samples, thereby effectively protecting the security of data.Type: ApplicationFiled: July 29, 2022Publication date: December 1, 2022Inventors: Jiankai Sun, Weihao Gao, Hongyi Zhang, Chong Wang, Junyuan Xie, Liangchao Wu, Xiaobing Liu
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Publication number: 20220366312Abstract: Disclosed in the embodiments of the present invention are a method and an apparatus for training an item coding model. The method comprises: acquiring an initial item coding model and a training sample set; using sample user information of training samples in the training sample set as the input for the initial item coding model to obtain the probability of sample item coding information corresponding to the inputted sample user information; adjusting the structural parameters of the initial item coding model to train an item coding model, the item coding model being used for characterizing the correspondence between inputted sample user information and sample item coding information and the correspondence between sample item information and sample item coding information. The present embodiment can use the trained item coding model to implement item recommendation and can use the item coding information as an index to increase retrieval efficiency.Type: ApplicationFiled: July 29, 2022Publication date: November 17, 2022Inventors: Weihao GAO, Xiangjun FAN, Jiankai SUN, Wenzhi Xiao, Chong WANG, Xiaobing LIU