Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Baidu Online Network Technology (Beijing) Co., Ltd.

Honeywell International Inc.

Kabushiki Kaisha Toshiba

Boe Technology Group Co., Ltd.

Hefei Xinsheng Optoelectronics Technology Co., Ltd.

Other

Wuhan China Star Optoelectronics Semiconductor Display Technology Co., Ltd.

Beijing Boe Technology Development Co., Ltd.

Baidu USA LLC

Hefei Boe Optoelectronics Technology Co., Ltd.

Utopus Insights, Inc.

Beijing Jiaotong University

Baidu Online Network Technology Co., Ltd.

Haifeng Wang

Hua Wu

Jizhou Huang

Tian Wu

Miao Fan

Dianhai Yu

Zhuo Chen

Yu Sun

Yanjun Ma

Zhongjun He

Ping Li

Hao Tian

Yu Ma

Mingming Sun

Yibing Liang

Hao Xiong

Xiaoguang Hu

Wei He

Errui Ding

Deguo Xia

Shuohuan Wang

Zhi Hong Li

Jing Liu

Yong Zhu

Ruiqing Zhang

Shiqi Zhao

Jingchao Feng

Chao Feng

Wenbin Jiang

Wei Zhang

Guoqing Wang

Quan Wang

Junyu Han

Liming Xia

Kaiwen Feng

Han Zhang

Fan Wang

Ke Wei Sun

Jingdong Wang

Xinyan Xiao

Chengliang Deng

Hongyu Liu

Jingtuo Liu

Hao Zhang

Haiming Wang

Lei Guo

Ying Li

Chen Zhao

Lei Jia

Zhou Chen

Liangliang Jiang

Zhi Li

Ning Qu

Jingzhou He

Chengquan Zhang

Jian Wang

Wei Li

Kai Liu

Hao Sun

Yang Liu

Shun Zhang

Tao Sun

Hui Zhao

Jun Xu

Qiang Fu

Wen Jun Yin

Meng Zhang

Qiang Li

Yan Zhang

Yi Liu

Jin Dong

Di Wang

Wei Zhou

Xiaogang Wang

Bo Zhou

Fengzhen Lv

Xing Li

Yongjun Yoon

Ji Dong Li

Qian Xu

Shuo Liu

Fei Long

Xiao Guang Rui

Zhigang Wang

Zhizhong Tu

Yongcan Wang

Qi Li

Sheng Qian

Fei Yu

Yong Jun Yoon

Dejing Dou

Qiaoqiao She

Xiaoyan Wang

Jing Song

Junjie Li

Shoucheng Wang

Shanshan Liu

Jingbo Zhou

Tingting Li

Yulin Li

The present disclosure provides an elevator control method, apparatus, electronic device, storage medium and system, which relate to artificial intelligence technology. The method includes: obtaining a user instruction, where the user instruction is determined by a speech device based on a user's speech content; recognizing the user instruction and converting the user instruction into an elevator control instruction; and controlling an elevator via the elevator control instruction. The elevator control method provided by the present disclosure enables the user to control the elevator through the speech device without touching the elevator, and thereby reducing potential safety risk caused by multiple people touching the elevator.

Elevator control method, apparatus, electronic device, storage medium and system

A method for processing behavior data, a method for controlling an autonomous vehicle, apparatuses thereof, a device, a storage medium, a computer program product, and an autonomous vehicle are provided. The method includes: acquiring historical driving data, the historical driving data comprising lane-level navigation data; and performing data mining on the historical driving data to obtain driving feature information, the driving feature information comprising at least one of: a lane-change position feature, a traveling speed feature, or a traveling path feature.

Method for processing behavior data, method for controlling autonomous vehicle, and autonomous vehicle

The disclosure provides an access method, an access apparatus, an electronic device and a computer storage medium, and relates to a field of computer technologies, in particular to a field of artificial intelligence technologies such as chip and deep learning. The method includes: determining a computational graph for calling an access device based on operator representations in a target model; optimizing the computational graph based on information of the access device; and performing relevant running operations of the target model on the access device based on the computational graph and an interface for the access device to access to a model framework of the target model, the interface being determined based on kit data of the access device.

Computational graph optimization and access mode selection for hetergeneous hardware

A method for processing a speech includes: acquiring an original speech; extracting a spectrogram from the original speech; acquiring a speech synthesis model, where the speech synthesis model comprises a first generation sub-model and a second generation sub-model; generating a harmonic structure of the spectrogram, by invoking the first generation sub-model to process the spectrogram; and generating a target speech, by invoking the second generation sub-model to process the harmonic structure and the spectrogram.

Method and apparatus for processing speech, electronic device and storage medium

A display method, a method of training a semantic unit detection model, an electronic device, and a storage medium, which relate to a field of artificial intelligence technology, in particular to fields of natural language processing and machine translation technologies. The display method includes: acquiring a language sequence to be displayed; dividing the language sequence to be displayed into a plurality of semantic units with semantics; and converting the plurality of semantic units into subtitles for display one by one.

Display method, method of training semantic unit detection model, electronic device, and storage medium

A method is provided. The method includes converting a search request of a user into a first request semantic vector. The method further includes searching a search resource database for at least one first data semantic vector matched with the first request semantic vector, wherein the search resource database is constructed as a semantic vector space in which different types of data are converted into corresponding data semantic vectors, and the different types of data include at least texts, pictures and videos. The method further includes generating, based on the at least one first data semantic vector, a search result.

Search method, computing device and storage medium

The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.

Character recognition model training method and apparatus, character recognition method and apparatus, device and storage medium

A scheduling method for a deep learning framework, a scheduling apparatus, an electronic device, a storage medium, and a program product is provided, and can be used in the field of artificial intelligence, especially in the fields of machine learning, deep learning, etc. The method includes: receiving a processing request for processing a plurality of tasks by using a dedicated processing unit, the processing request including scheduling requirements for the plurality of tasks, and each of the plurality of tasks being associated with execution of multi-batch data processing; and scheduling, based on the scheduling requirements for the plurality of tasks in batches of data, the dedicated processing unit to process the plurality of tasks.

Deep learning framework scheduling

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

Embodiments of the present disclosure provide a method and apparatus of training a model, an electronic device, a storage medium and a development system, which relate to a field of deep learning. The method may include calling a training preparation component to set at least a loss function and an optimization function for training the model, in response to determining that a training preparation instruction is received. The method further includes calling a training component to set a first data reading component, in response to determining that a training instruction is received. The first data reading component is configured to load a training data set for training the model. In addition, the method may further include training the model based on the training data set from the first data reading component, by using the loss function and the optimization function through the training component.

Model development tool to train, evaluate and predict with deep learning by selecting functions

The present application discloses a distributed training method and system, a device and a storage medium, and relates to technical fields of deep learning and cloud computing. The method includes: sending, by a task information server, a first training request and information of an available first computing server to at least a first data server; sending, by the first data server, a first batch of training data to the first computing server, according to the first training request; performing, by the first computing server, model training according to the first batch of training data, sending model parameters to the first data server so as to be stored after the training is completed, and sending identification information of the first batch of training data to the task information server so as to be recorded; wherein the model parameters are not stored at any one of the computing servers.

Distributed training method and system, device and storage medium

An information search method includes: obtaining search words at least including a question to be searched and obtaining an initial text vector representation of the search words; obtaining a video corresponding to the search words, and obtaining multi-modality vector representations of the video; starting from the initial text vector representation, performing N rounds of interaction between the video and the search words based on the multi-modality vector representations and a text vector representation of the search words of a current round, to generate a target fusion vector representation, where N is an integer greater than or equal to 1; and obtaining target video frames matching the question to be searched by annotating the video based on the target fusion vector representation.

Information search method and device, electronic device, and storage medium

A method and apparatus of constructing a network model for deep learning, a device, and a storage medium, which relate to artificial intelligence, and in particular to a field of deep learning. The method of constructing the network model for deep learning includes: determining an execution mode for executing codes, based on a mode parameter; executing the codes by using a first component, which is executable in a first execution mode, through a syntax element in the codes, in response to determining that the execution mode is the first execution mode; and executing the codes by using a second component, which is executable in a second execution mode, through the syntax element, in response to determining that the execution mode is the second execution mode; wherein the first component and the second component have the same component interface, and the syntax element corresponds to the component interface.

Method of constructing network model for deep learning, device, and storage medium

A method for pre-training a language model includes: constructing a pre-training language data set, in which the pre-training language data set comprises unsupervised language data and supervised language data; generating a hierarchical multi-template and multi-task language data set based on the pre-training language data set; and pre-training the language model based on the hierarchical multi-template and multi-task language data set.

Method for pre-training language model

Provided are a joint perception model training method, a joint perception method, a device, and a storage medium. The joint perception model training method includes: acquiring sample images and perception tags of the sample images; acquiring a preset joint perception model, where the joint perception model includes a feature extraction network and a joint perception network; performing feature extraction on the sample images through the feature extraction network to obtain target sample features; performing joint perception through the joint perception network according to the target sample features to obtain perception prediction results; and training the preset joint perception model according to the perception prediction results and the perception tags, where the joint perception includes executing at least two perception tasks.

Joint perception model training method, joint perception method, device, and storage medium

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Beijing, China

Haifeng Wang