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公开(公告)号：US20250094877A1
公开(公告)日：2025-03-20
申请号：US18969719
申请日：2024-12-05
Inventor： Fan WANG , Hua WU , Yingzhan LIN , Zengfeng ZENG , Yufeng HU , Jianhui DING , Haifeng WANG
IPC: G06N20/00
Abstract: A large model-based method of generating a text, a method of training a text generation model, a device, and a medium are provided, which relate to a field of artificial intelligence technology, specifically to fields of deep learning, natural language processing and large model technologies. The large model-based method of generating a text includes: acquiring a memory state for a text to be processed, where the memory state is generated based on a previous text of the text to be processed; determining an embedding feature of the text to be processed as an initial hidden state, and processing the memory state and the initial hidden state by using a first attention mechanism to obtain an updated hidden state; and generating a subsequent text for the text to be processed based on the updated hidden state.
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公开(公告)号：US20230243661A1
公开(公告)日：2023-08-03
申请号：US17999917
申请日：2022-04-28
Inventor： Jizhou HUANG , Kejiao LI , Bo ZHOU , Fan WANG , Jingzhou HE , Haifeng WANG
CPC classification number: G01C21/3461 , G01C21/3492 , G08G1/0108
Abstract: Provided are a navigation path planning method and apparatus, a device, and a storage medium. The navigation path planning method includes planning at least two available navigation paths for each target user of at least two target users in a target region; and determining a global passing feature of the target region and selecting, according to the global passing feature of the target region, one available navigation path from the at least two available navigation paths corresponding to each target user to serve as a recommended navigation path to be recommended to each target user.
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公开(公告)号：US20230115984A1
公开(公告)日：2023-04-13
申请号：US18064812
申请日：2022-12-12
Inventor： Zhiyuan CHEN , Xiaomin FANG , Fan WANG , Jingzhou HE
Abstract: The present disclosure provides a method for training a model, a method and an apparatus for generating molecules, and relates to the technical field of computer technology, particularly the technical field of artificial intelligence. The particular implementation may include: obtaining first molecular samples and second molecular samples; determining molecular difference information based on the first molecular samples and the second molecular samples; training an initial encoding module and an initial generation module based on the molecular difference information to obtain a target encoding module and a target generation module; and determining a molecule generation model based on the target encoding module and the target generation module.
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公开(公告)号：US20230004862A1
公开(公告)日：2023-01-05
申请号：US17537575
申请日：2021-11-30
Inventor： Yingfei XIANG , Hongyu LUO , Xiaomin FANG , Fan WANG
IPC: G06N20/00
Abstract: The technical solution relates to the field of artificial intelligence technologies, such as machine learning technologies, natural language processing technologies, or the like. A plurality of training samples are collected, each of the plurality of training samples includes information of a known training target protein, information of two training drugs, and a real difference between affinities of the two training drugs for the known training target. The ranking learning model is trained with the plurality of training samples, such that the ranking learning model learns a capability of predicting a magnitude relationship between the affinities of the two training drugs for the known training target protein in each of the plurality of training samples.
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公开(公告)号：US20220392585A1
公开(公告)日：2022-12-08
申请号：US17820688
申请日：2022-08-18
Inventor： Shanzhuo ZHANG , Lihang LIU , Yueyang HUANG , Donglong HE , Xiaomin FANG , Xiaonan ZHANG , Fan WANG , Jingzhou HE
Abstract: A method and apparatus for training a compound property prediction model, a device, a storage medium and a program product. A implementation of the method comprises: acquiring an unannotated compound data set; pre-training a graph neural network using the unannotated compound data set to obtain a pre-trained graph neural network; acquiring a plurality of annotated compound data sets, each annotated compound data set being annotated with one kind of compound property; and performing multi-task training on the pre-trained graph neural network using the plurality of annotated compound data sets, to obtain a compound property prediction model, the compound property prediction model being used to predict a plurality kinds of properties of a compound.
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Patent Agency Ranking
公开(公告)号：US20220231504A1
公开(公告)日：2022-07-21
申请号：US17684131
申请日：2022-03-01
Inventor： Hongsheng ZENG , Bo ZHOU , Kejiao LI , Fan WANG , Yongfeng CHEN , Jingzhou HE
Abstract: A method for training a power system scheduling model includes: generating a plurality of first scheduling sub-models based on a first initial scheduling model; acquiring a first matching degree of historical running state information and each of candidate actions, output by each of the plurality of first scheduling sub-models, by inputting the historical running state information into each of the plurality of first scheduling sub-models; generating a second initial scheduling model by correcting the first initial scheduling model based on first matching degrees corresponding to each of the plurality of first scheduling sub-models; and returning to the generating the plurality of first scheduling sub-models based on the second initial scheduling model, until the matching degree output by the second initial scheduling module meets the convergence condition, determining the second initial scheduling model as the power system scheduling model.
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公开(公告)号：US20240246575A1
公开(公告)日：2024-07-25
申请号：US18606329
申请日：2024-03-15
Inventor： Jizhou HUANG , Fan WANG
CPC classification number: B60W60/0027 , B60W50/0097 , G06N3/08 , B60W2556/10
Abstract: An autonomous driving method implemented by using an automatic driving model is provided. The autonomous driving model comprises a multimodal encoding layer and a decision control layer. The autonomous driving method includes: obtaining first input information of the multimodal encoding layer; inputting the first input information into the multimodal encoding layer to obtain an implicit representation corresponding to the first input information output by the multimodal encoding layer; and inputting second input information including the implicit representation into the decision control layer to obtain target autonomous driving strategy information output by the decision control layer.
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公开(公告)号：US20230032324A1
公开(公告)日：2023-02-02
申请号：US17966127
申请日：2022-10-14
Inventor： Fan WANG , Hao TIAN , Haoyi XIONG , Hua WU , Jingzhou HE , Haifeng WANG
IPC: G06N20/00
Abstract: A method for training a decision-making model parameter, a decision determination method, an electronic device, and a non-transitory computer-readable storage medium are provided. In the method, a perturbation parameter is generated according to a meta-parameter, and first observation information of a primary training environment is acquired based on the perturbation parameter. According to the first observation information, an evaluation parameter of the perturbation parameter is determined. According to the perturbation parameter and the evaluation parameter thereof, an updated meta-parameter is generated. The updated meta-parameter is determined as a target meta-parameter, when it is determined, according to the meta-parameter and the updated meta-parameter, that a condition for stopping primary training is met. According to the target meta-parameter, a target memory parameter corresponding to a secondary training task is determined, where the target memory parameter and the target meta-parameter are used to make a decision corresponding to a prediction task.
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9.发明申请公开(公告)号：US20230005572A1
公开(公告)日：2023-01-05
申请号：US17687809
申请日：2022-03-07
Inventor： Zhiyuan CHEN , Xiaomin FANG , Fan WANG
Abstract: A molecular structure acquisition method, an electronic device and a storage medium, which relate to the field of artificial intelligence such as deep learning, are disclosed. The method may include: performing, for an initial seed, the following first processing: generating M molecular structures according to the seed, M being a positive integer greater than one; taking the M molecular structures as candidate molecular structures, and selecting some molecular structures from the candidate molecular structures as progeny molecular structures; and performing evolutionary learning on the progeny molecular structures, taking the progeny molecular structures after evolutionary learning as the seed, and repeating the first processing until convergence reaches an optimization objective, and when the convergence reaches the optimization objective, a newly selected molecular structure is taken as a desired molecular structure.
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公开(公告)号：US20220215899A1
公开(公告)日：2022-07-07
申请号：US17557691
申请日：2021-12-21
Inventor： Fan WANG , Jingzhou HE , Xiaomin FANG , Xiaonan ZHANG , Hua WU , Tian WU , Haifeng WANG
Abstract: The present disclosure discloses an affinity prediction method and apparatus, a method and apparatus for training an affinity prediction model, a device and a medium, and relates to the field of artificial intelligence technologies, such as machine learning technologies, smart medical technologies, or the like. An implementation includes: collecting a plurality of training samples, each training sample including information of a training target, information of a training drug and a test data set corresponding to the training target; and training an affinity prediction model using the plurality of training samples. In addition, there is further disclosed the affinity prediction method. The technology in the present disclosure may effectively improve accuracy and a training effect of the trained affinity prediction model. During an affinity prediction, accuracy of a predicted affinity of a target to be detected with a drug to be detected may be higher by acquiring a test data set corresponding to the target to be detected to participate in the prediction.
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