Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Jingzhou He

Fan Wang

Zhihong Fu

Lei Shi

Guyue Zhou

Qiugen Xiao

Liqun Zheng

Hua Wu

Di Jiang

Siqi Bao

Xiaomin Fang

Jinbo Zhan

Haifeng Wang

Xinyu Wang

Chen Bo Li

Shikun Feng

Huang He

Kunsheng Zhou

Lihang Liu

Zhuobin Deng

Pengkai Li

Yan Zhang

Jizhou Huang

Chao Zhou

Bo Zhou

Rui Chen

Shuo Huang

Pu Wang

Shiwei Huang

Yang Xue

Wei Bi

Meng Liao

Yuhui Cao

Lingke Zeng

Jiaxin Lin

Zhisheng Wang

Tixi He

Yingbin Su

Zhihui Liu

Xianwei Xin

Zhifan Zhu

Mengdi Xu

Jiawei Lai

Yibin Wei

Junkun Chen

Kejiao Li

Yelan Mo

Xinxian Huang

Xiyi Luo

Yaxue Chen

Zengfeng Zeng

A 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 by using a path planning algorithm; 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, where the global passing feature of the target region refers to a global passing feature of the at least two target users in the target region and is used for cooperative path planning that takes into account a global passing time length in the target region.

Navigation path planning method and apparatus, device, and storage medium

The present application discloses an optimizer learning method and apparatus, an electronic device and a readable storage medium, which relates to the field of deep learning technologies. An implementation solution adopted by the present application during optimizer learning is: acquiring training data, the training data including a plurality of data sets each including neural network attribute information, neural network optimizer information, and optimizer parameter information; and training a meta-learning model by taking the neural network attribute information and the neural network optimizer information in the data sets as input and taking the optimizer parameter information in the data sets as output, until the meta-learning model converges. The present application can implement self-adaptation of optimizers, so as to improve generalization capability of the optimizers.

Optimizer learning method and apparatus, electronic device and readable storage medium

A method for dialogue processing, an electronic device and a storage medium are provided. The specific technical solution includes: obtaining a dialogue history; selecting a target machine from a plurality of machines; inputting the dialogue history into a trained dialogue model in the target machine to generate a response to the dialogue history, in which the dialogue model comprises a common parameter and a specific parameter, and different machines correspond to the same common parameter.

Method for dialogue processing, electronic device and storage medium

The disclosure discloses a method for generating a conversation, an electronic device, and a storage medium. The detailed implementation includes: obtaining a current conversation and historical conversations of the current conversation; selecting multiple reference historical conversations from the historical conversations and adding the multiple reference historical conversations to a temporary conversation set; and generating reply information of the current conversation based on the current conversation and the temporary conversation set.

Method for generating conversation reply information using a set of historical conversations, electronic device, and storage medium

A method and apparatus for optimizing a recommendation system, a device and a computer storage medium are described, which relates to the technical field of deep learning and intelligent search in artificial intelligence. A specific implementation solution is: taking the recommendation system as an agent, a user as an environment, each recommended content of the recommendation system as an action of the agent, and a long-term behavioral revenue of the user as a reward of the environment; and optimizing to-be-optimized parameters in the recommendation system by reinforcement learning to maximize the reward of the environment. The present disclosure can effectively optimize long-term behavioral revenues of users.

Method and apparatus for optimizing recommendation system, device and computer storage medium

An active interaction method, an electronic device and a readable storage medium, relating to the field of deep learning and image processing technologies, are disclosed. According to an embodiment, the active interaction method includes: acquiring a video shot in real time; extracting a visual target from each image frame of the video, and generating a first feature vector of each visual target; for each image frame of the video, fusing the first feature vector of each visual target and identification information of the image frame to which the visual target belongs to generate a second feature vector of each visual target; aggregating the second feature vectors with the same identification information respectively to generate a third feature vector corresponding to each image frame; and initiating active interaction in response to determining that the active interaction is to be performed according to the third feature vector of a preset image frame.

Active interaction method, electronic device and readable storage medium

Embodiments of the present disclosure relate to a method and apparatus for generating a dialogue model. The method may include: acquiring a corpus sample set, a corpus sample including input information and target response information; classifying corpus samples in the corpus sample set, setting discrete hidden variables for the corpus samples based on a classification result to generate a training sample set, a training sample including the input information, the target response information, and a discrete hidden variable; and training a preset neural network using the training sample set to obtain the dialogue model, the dialogue model being used to represent a corresponding relationship between inputted input information and outputted target response information.

Method and apparatus for generating dialogue model

A method and apparatus for correcting character errors, an electronic device and a storage medium are disclosed, which relates to the natural language processing field and the deep learning field. The method may include: for a character to be processed, acquiring the score of each character in a pre-constructed vocabulary, the score being a score of the reasonability of the character in the vocabulary at the position of the character to be processed; selecting top K characters as candidates of the character to be processed, K being a positive integer greater than one; selecting an optimal candidate from the K candidates; and replacing the character to be processed with the optimal candidate if the optimal candidate is different from the character to be processed. With the solution of the present application, the accuracy of an error correction result, or the like, may be improved.

Method and apparatus for correcting character errors, electronic device and storage medium

Embodiment of the disclosure provide a method and apparatus for generating information. The method includes: acquiring vectors of a plurality of users, the vector being used to characterize behavior habits of the users; inputting the vectors of the plurality of users and push information pushed by a push system to the plurality of users into a feedback information generating model established in advance, to generate the feedback information of the plurality of users for the push information, wherein the feedback information generating model is used to characterize a corresponding relationship between the vectors, the push information and the feedback information; and generating an evaluation report of the push system based on the feedback information.

Method and apparatus for generating information

Embodiments of the present disclosure disclose a method, an apparatus, a server, and a storage medium for recalling for a search. The method for recalling for a search includes: acquiring a search term inputted by a user; calculating a semantic vector of the search term using a pre-trained neural network model; and recalling, according to a pre-established index, target documents related to the semantic vector of the search term from candidate documents, the index being established based on the semantic vectors of the candidate documents, and the semantic vectors of the candidate documents being calculated using the pre-trained neural network model. The embodiments of the present disclosure may solve a problem in the existing method for recalling that the recalling accuracy is affected by failing to generalize semantics, to improve the accuracy of recalling for a search.

Method, apparatus, server, and storage medium for recalling for search

A method and device for correcting an error in a text are provided. The method includes: preprocessing the text to obtain at least one segment of the text; generating a plurality of candidate segments for the segment; scoring the plurality of candidate segments with a tree model, to obtain respective first scoring results of the plurality of candidate segments; scoring the plurality of candidate segments with a deep neural network model, to obtain respective second scoring results of the plurality of candidate segments; for each candidate segment, calculating a scoring of the candidate segment based on the first scoring result and the second scoring result of the candidate segment; ranking the plurality of candidate segments according to the scorings of the candidate segments, to obtain a ranking result; and correcting the error in the text according to the ranking result.

Method and device for correcting error in text

A method and an apparatus for correcting a query based on artificial intelligence, including: receiving a first query input by a user, and judging whether the first query satisfies an error correcting condition according to a preset error correcting strategy; determining a first segment to be corrected in the first query if the first query satisfies the error correcting condition; acquiring one or more first candidate results corresponding to the first segment according to a preset candidate recalling strategy; determining an error correcting result corresponding to the first segment according to quality feature values of the one or more first candidate results; and performing an error correction on the first query according to the error correcting result, and generating a second query.

Method and apparatus for correcting query based on artificial intelligence

Disclosed are a method and an apparatus for constructing a binary feature dictionary. The method may include: extracting binary features from a corpus; calculating a preset statistic of each binary feature; and selecting a preset number of binary features in sequence according to the preset statistic to constitute the binary feature dictionary.

Method and apparatus for constructing binary feature dictionary

A searching method and a searching apparatus based on a neural network and a search engine are disclosed, the searching method including: acquiring a query and a pre-query input by a user; acquiring a plurality of search results according to the query; generating a target term vector representation according to the query, the pre-query and the plurality of search results based on an MLP; and forecasting the target term vector representation based on a semantic model of a deep neural network so as to acquire a plurality of s optimized search results corresponding to the query.

Searching method and searching apparatus based on neural network and search engine

The present disclosure provides a method and an apparatus for providing information based on artificial intelligence. The method includes: determining a characteristic of interest of the user according to historical access records of the user; and displaying an information card matched with the characteristic of interest in an information displaying interface, the information card including core contents of news matched with the characteristic of interest determined by analyzing news in a database.

Method and apparatus for providing information based on artificial intelligence

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Beijing, China

Jingzhou He