Patents by Inventor Jiayuan SUN
Jiayuan 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: 11640550Abstract: The disclosure discloses a method and apparatus for updating a deep learning model. An embodiment of the method comprises: executing following updating: acquiring a training dataset under a preset path, training a preset deep learning model based on the training dataset to obtain a new deep learning model; updating the preset deep learning model to the new deep learning model; increasing training iterations; determining whether a number of training iterations reaches a threshold of training iterations; stopping executing the updating if the number of training iterations reaches the threshold of training iterations; and continuing to execute the updating after an interval of a preset time length if the number of training iterations fails to reach the threshold of training iterations. This embodiment has improved the model updating efficiency.Type: GrantFiled: July 3, 2018Date of Patent: May 2, 2023Assignee: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.Inventors: Lan Liu, Faen Zhang, Kai Zhou, Qian Wang, Kun Liu, Yuanhao Xiao, Dongze Xu, Tianhan Xu, Jiayuan Sun
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Patent number: 11315034Abstract: A system comprises: a data warehouse, a storage device and a cluster including a plurality of computing nodes; the data warehouse is configured to store task data obtained from the user; at least one computing node in the cluster includes a resource scheduling component, and is configured to perform resource scheduling for the task and determine a computing node executing the task; the computing node executing the task comprises a model training component and/or a prediction component; the model training component is configured to, according to task data, invoke a corresponding type of learning model from the storage device; use sample data and training target included in the task data to train the learning model, to obtain the prediction model corresponding to the task and store the prediction model in the storage device; the prediction component is configured to obtain a prediction result output by the prediction model.Type: GrantFiled: August 30, 2018Date of Patent: April 26, 2022Assignee: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.Inventors: Kai Zhou, Qian Wang, Faen Zhang, Kun Liu, Yuanhao Xiao, Dongze Xu, Tianhan Xu, Jiayuan Sun, Lan Liu
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Patent number: 11194999Abstract: An integrated facial recognition method and system. The method includes: receiving a request for acquiring an integrated facial recognition service sent by a user terminal, which includes: an identifier of a user-selected model associated with facial recognition of the user, and an identifier of an operation selected by the user from candidate operations; and executing distributedly an operation selected by the user from the candidate operations on the user-selected model associated with the facial recognition of the user to obtain an operation result, and storing the operation result. The embodiment has realized completing the operations such as training a model or developing a facial recognition application, without the need of buying hardware and establishing a software environment by the user, thereby saving the development cost and improving the convenience of using the facial recognition service.Type: GrantFiled: August 14, 2018Date of Patent: December 7, 2021Assignee: Beijing Baidu Netcom Science and Technology Co., Ltd.Inventors: Tianhan Xu, Faen Zhang, Kai Zhou, Qian Wang, Kun Liu, Yuanhao Xiao, Dongze Xu, Jiayuan Sun, Lan Liu
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Patent number: 11055602Abstract: The present disclosure provides a deep learning assignment processing method and apparatus, a device and a storage medium. It is feasible to obtain the deep learning assignment submitted by the user in a predetermined manner, the predetermined manner comprising the web UI manner, then submit the deep learning assignment to the deep learning system so that the deep learning system runs the submitted deep learning assignment. As compared with the prior art, processing such as programming is not needed upon submitting the deep learning assignment in the solutions of the present disclosure, thereby simplifying the user's operations, improving the processing efficiency of the deep learning assignment, and accelerating the user's speed of developing deep learning.Type: GrantFiled: October 12, 2018Date of Patent: July 6, 2021Assignee: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.Inventors: Dongze Xu, Faen Zhang, Kai Zhou, Qian Wang, Kun Liu, Yuanhao Xiao, Jiayuan Sun, Lan Liu, Tianhan Xu
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Publication number: 20190114527Abstract: The present disclosure provides a deep learning assignment processing method and apparatus, a device and a storage medium. It is feasible to obtain the deep learning assignment submitted by the user in a predetermined manner, the predetermined manner comprising the web UI manner, then submit the deep learning assignment to the deep learning system so that the deep learning system runs the submitted deep learning assignment. As compared with the prior art, processing such as programming is not needed upon submitting the deep learning assignment in the solutions of the present disclosure, thereby simplifying the user's operations, improving the processing efficiency of the deep learning assignment, and accelerating the user's speed of developing deep learning.Type: ApplicationFiled: October 12, 2018Publication date: April 18, 2019Inventors: Dongze XU, Faen ZHANG, Kai ZHOU, Qian WANG, Kun LIU, Yuanhao XIAO, Jiayuan SUN, Lan LIU, Tianhan XU
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Publication number: 20190087383Abstract: A system comprises: a data warehouse, a storage device and a cluster including a plurality of computing nodes; the data warehouse is configured to store task data obtained from the user; at least one computing node in the cluster includes a resource scheduling component, and is configured to perform resource scheduling for the task and determine a computing node executing the task; the computing node executing the task comprises a model training component and/or a prediction component; the model training component is configured to, according to task data, invoke a corresponding type of learning model from the storage device; use sample data and training target included in the task data to train the learning model, to obtain the prediction model corresponding to the task and store the prediction model in the storage device; the prediction component is configured to obtain a prediction result output by the prediction model.Type: ApplicationFiled: August 30, 2018Publication date: March 21, 2019Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.Inventors: Kai ZHOU, Qian WANG, Faen ZHANG, Kun LIU, Yuanhao XIAO, Dongze XU, Tianhan XU, Jiayuan SUN, Lan LIU
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Publication number: 20190080154Abstract: An integrated facial recognition method and system. The method includes: receiving a request for acquiring an integrated facial recognition service sent by a user terminal, which includes: an identifier of a user-selected model associated with facial recognition of the user, and an identifier of an operation selected by the user from candidate operations; and executing distributedly an operation selected by the user from the candidate operations on the user-selected model associated with the facial recognition of the user to obtain an operation result, and storing the operation result. The embodiment has realized completing the operations such as training a model or developing a facial recognition application, without the need of buying hardware and establishing a software environment by the user, thereby saving the development cost and improving the convenience of using the facial recognition service.Type: ApplicationFiled: August 14, 2018Publication date: March 14, 2019Inventors: Tianhan Xu, Faen Zhang, Kai Zhou, Qian Wang, Kun Liu, Yuanhao Xiao, Dongze Xu, Jiayuan Sun, Lan Liu
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Publication number: 20190012575Abstract: The present disclosure discloses a method, apparatus and system for updating a deep learning model. A specific embodiment of the method includes: receiving a new training data set sent by a client, the new training data set being detected by the client in a preset path; training a preset deep learning model based on the new training data set to obtain a trained prediction model; and updating the preset deep learning model to the prediction model so that the prediction model is used to perform a data prediction operation online. This embodiment realizes the docking with the training data set of the user and improves the update efficiency of the deep learning model.Type: ApplicationFiled: July 3, 2018Publication date: January 10, 2019Inventors: Yuanhao XIAO, Faen ZHANG, Kai ZHOU, Qian WANG, Kun LIU, Dongze XU, Tianhan XU, Jiayuan SUN, Lan LIU
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Publication number: 20190012576Abstract: The disclosure discloses a method and apparatus for updating a deep learning model. An embodiment of the method comprises: executing following updating: acquiring a training dataset under a preset path, training a preset deep learning model based on the training dataset to obtain a new deep learning model; updating the preset deep learning model to the new deep learning model; increasing training iterations; determining whether a number of training iterations reaches a threshold of training iterations; stopping executing the updating if the number of training iterations reaches the threshold of training iterations; and continuing to execute the updating after an interval of a preset time length if the number of training iterations fails to reach the threshold of training iterations. This embodiment has improved the model updating efficiency.Type: ApplicationFiled: July 3, 2018Publication date: January 10, 2019Inventors: Lan LIU, Faen ZHANG, Kai ZHOU, Qian WANG, Kun LIU, Yuanhao XIAO, Dongze XU, Tianhan XU, Jiayuan SUN