Beijing Baidu Netcom Science and Technology Co., Ltd.

International Business Machines Corporation

Huawei Technologies Co., Limited

Sony Corporation

Beijing Baidu Netcom Science Technology Co., Ltd

Other

Chinese Academy of Sciences

Beijing Bytedance Network Technology Co., Ltd.

Dalian University of Technology

Huawei Digital Power Technologies Co., Ltd.

Samsung Electronics Co., Ltd.

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

China Petroleum Chemical Corporation

Shenzhen Mindray Bio-medical Electronics Co., Ltd.

Spreadtrum Communications (shanghai) Co., Ltd.

Yu Sun

Haifeng Wang

Hua Wu

Hao Tian

Zhongfeng Li

Yi Qin

Xiaodong Xu

Dianhai Yu

Rui Gao

Qimei Cui

Ningyu Chen

Jiahui Liu

Shengyue Dou

Han Zhang

Zhen Li

Richard Walton Ragan, Jr

Chao Wei

Wei Shan Dong

Xiang Sean Zhou

Wen Liu

Yi Liu

Haibao Ren

Yi Ren

Jie Hui Xu

Yang Zhao

Zhi Hong Li

Jue Wang

Ting Ting Zhang

Sheng Zhu

Jin Xu

Yu Wang

Jun Luo

Jing Liu

Leiming Zhang

Chi Zhang

Yan Peng

John Falk Kelley

Xinxian Li

Tian Wu

Huayan Pu

Qun Cao

Jing Zhou

Shuohuan Wang

Yukun Li

Chao Pang

Siyu Ding

Li Yue

Lingyan Wu

Qian Su

Wei You

Xiaoming Wang

Kang Ed Zhang

Qing Chang

Qiaoqiao She

Ci-Wei Lan

Jing Shi

Wenyu Wang

Shanyang Ke

Yuan Bao

Jian Wen Chi

Dong Ni

Shikun Feng

Yiping Qu

Xun Ma

Guofeng Han

Jiaxiang Liu

Shaorong Xie

Ning Wu

Ming Wu

Chaopeng Liu

Jingjing Zhang

Songtao Zhan

Feng Shi

Jiang Yu

Qin Yue Chen

Fei Yu

Lei Jin

Jian Cen

Qi Liang

Feifei Li

Xu Chu Jiang

Liming Xin

Xiaoyu Wu

Ke Chen

Wei Shui

Han Su

Ji Liu

Siqi Wang

Gui Yu Jiang

Zhongyao Yu

Haoyu Wang

Ao Zhang

Dajun Deng

Da Wei Zhang

Ying Ge

Hua Bai

Yiming Xiong

Jianwei Fan

Cuibo Yu

Liyao Wei

A method for generating a target object is provided. A first discrete encoded sequence corresponding to an original object is generated by performing discrete encoding on the original object. The original object is of an image type, a text type, or a text-image-combined type. A second discrete encode sequence is obtained by inputting the first discrete encoded sequence into a generative model. A target object is generated based on the second discrete encoded sequence. The target object is of an image type or a text type. When the original object is of the image type, the target object is of the text type. When the original object is of the text type, the target object is of the image type.

Method for generating target object, 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

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

A method and apparatus for pre-training a model, a device, a storage medium, and a program product. An embodiment of the method includes: acquiring a sample natural language text; generating N types of prompt words based on the sample natural language text, where N is a positive integer; generating sample input data based on the sample natural language text and the N types of prompt words; and training an initial language model based on the sample input data, to obtain a pre-trained language model.

Method for pre-training model, device, and storage medium

A model training method, a model training platform, an electronic device and a storage medium are provided, which can be used in the field of artificial intelligence, particularly the fields of natural language processing and deep learning. The model training method includes: receiving an input; determining, based on the input, a user-oriented prefabricated function; determining, based on the input, a model training function; determining, based on the input, a pre-trained model; determining, based on the input, a network structure associated with the pre-trained model so as to support use of the pre-trained model; training, based on the input, the model by using the prefabricated function, the model training function, and the pre-trained model; and providing an output associated with a trained model.

Model training

The present application discloses an enzymatic electrode system and its applications. The enzymatic electrode system comprises a working electrode, a counter electrode, and an optional reference electrode. The working electrode includes an electrode support substrate and a conductive substrate located at the top of the electrode support substrate. The surface or interior of the conductive substrate contains oxidoreductases, coenzyme reductases, and optional coenzymes. This application utilizes a comprehensive enzymatic electrode system incorporating oxidoreductases, coenzyme reductases, and coenzymes for the detection of specific substances in samples, such as glucose, lactate, ketones, and the like.

Enzymatic electrode system and its applications

A method for generating a cross-lingual textual semantic model includes: acquiring a set of training data that includes pieces of monolingual non-parallel text and pieces of bilingual parallel text; determining a semantic vector of each piece of text in the set of training data by inputting each piece of text into an initial textual semantic model; determining a distance between semantic vectors of each two pieces of text in the set of training data based on the semantic vector of each piece of text in the set of training data; determining a gradient modification based on a parallel relationship between each two pieces of text in the set of training data and the distance between the semantic vectors of each two pieces of text in the set of training data; and acquiring a modified textual semantic model by modifying the initial textual semantic model based on the gradient modification.

Method for generating cross-lingual textual semantic model, and electronic device

An information push method, apparatus, electronic device and storage medium provided by the embodiments of the present disclosure. A type identification corresponding to a media work is displayed when a first page of playing the media work is displayed, and the page is switched when a user triggers the type identification on the first page to display the second page corresponding to the type identification to the user. Since information of other media works corresponding to the type identification of the media work can be displayed on the second page, the user's demand for obtaining push information can be effectively met; at the same time, since the user can directly obtain the second page in a manner of direct triggering the type identification, and the obtaining operation process is very concise, then the solution provided by the present disclosure can optimize an user experience while improving information push efficiency.

Information push method, apparatus, electronic device and storage medium

The present application provides a method of training a natural language processing model, which relates to a field of artificial intelligence, and in particular to a field of natural language processing. A specific implementation scheme includes: performing a semantic learning for multi-tasks on an input text, so as to obtain a semantic feature for the multi-tasks, wherein the multi-tasks include a plurality of branch tasks; performing a feature learning for each branch task based on the semantic feature, so as to obtain a first output result for each branch task; calculating a loss for each branch task according to the first output result for the branch task; and adjusting a parameter of the natural language processing model according to the loss for each branch task. The present application further provides a method of processing a natural language, an electronic device, and a storage medium.

Method and apparatus of training natural language processing model, and method and apparatus of processing natural language

A photovoltaic system includes an inverter and a plurality of photovoltaic units connected to the inverter. Each photovoltaic unit includes a controller and one or more photovoltaic modules connected to the controller. The controller in each photovoltaic unit is further configured to obtain a power carrier signal sent by a controller in another photovoltaic unit of the plurality of photovoltaic units, determine an attenuation reference factor of the power carrier signal based on the obtained power carrier signal, and send the attenuation reference factor to the inverter. The inverter is further configured to group the plurality of photovoltaic units based on the attenuation degree of the power carrier signal obtained by each photovoltaic unit. This application can implement automatic grouping of photovoltaic units.

Photovoltaic system, photovoltaic unit grouping method, computing device, and storage medium

The disclosure relates to a method, apparatus, readable medium, and electronic device for live stream interaction. The method includes: in accordance with displaying of a first control at a first target position of a live stream interface, obtaining a first duration during which a user watches the live stream interface; in accordance with a determination that the first duration is greater than or equal to a first predetermined duration threshold, switching the first control to a second control, and displaying the second control at a second target position of the live stream interface; and in response to a first operation command performed by the user on the second control, allocating a virtual resource to the user. In this way, interaction modes of a live stream room are enriched, and an activity level of the live stream room can be improved.

Methods, apparatuses, readable media and electronic devices for live stream interaction

The present disclosure provides a method, apparatus, electronic device and storage medium for training a semantic similarity model, which relates to the field of artificial intelligence. A specific implementation solution is as follows: obtaining a target field to be used by a semantic similarity model to be trained; calculating respective correlations between the target field and application fields corresponding to each of training datasets in known multiple training datasets; training the semantic similarity model with the training datasets in turn, according to the respective correlations between the target field and the application fields corresponding to each of the training datasets. According to the technical solution of the present disclosure, it is possible to, in the fine-tuning phase, more purposefully train the semantic similarity model with the training datasets with reference to the correlations between the target field and the application fields corresponding to the training datasets, thereby effectively improving the learning capability of the sematic similarity model and effectively improving the accuracy of the trained semantic similarity model.

Method, apparatus, electronic device and storage medium for training semantic similarity model

An object processing method includes: displaying a first operation interface in response to triggering a first operation control of a first stage of a target object, the first operation interface including at least one first operation item; displaying a content input interface in response to completing an operation for the at least one first operation item, the content input interface including at least one input item; in response to completion of content input for the at least one input item, determining a second target value and generating a second operation control corresponding to a second stage of the target object; displaying a second operation interface in response to triggering the second operation control, the second operation interface including at least one second operation item; and updating the state of the target object to a completed state in response to completing an operation for the second operation item.

Object processing method having customization options and apparatus thereof

The disclosure discloses a method and an apparatus for generating a semantic representation model, and a storage medium. The detailed implementation includes: performing recognition and segmentation on the original text included in an original text set to obtain knowledge units and non-knowledge units in the original text; performing knowledge unit-level disorder processing on the knowledge units and the non-knowledge units in the original text to obtain a disorder text; generating a training text set based on the character attribute of each character in the disorder text; and training an initial semantic representation model by employing the training text set to generate the semantic representation model.

Method and apparatus for generating semantic representation model, and storage medium

The present disclosure provides a multi-lingual model training method, apparatus, electronic device and readable storage medium and relates to the technical field of deep learning and natural language processing. A technical solution of the present disclosure when training the multi-lingual model is: obtaining training corpuses comprising a plurality of bilingual corpuses and a plurality of monolingual corpuses; training a multi-lingual model with a first training task by using the plurality of bilingual corpuses; training the multi-lingual model with a second training task by using the plurality of monolingual corpuses; and completing the training of the multi-lingual model in a case of determining that loss functions of the first training task and second training task converge. In the present disclosure, the multi-lingual model can be enabled to achieve semantic interaction between different languages and improve the accuracy of the multi-lingual model in learning the semantic representations of the multi-lingual model.

Multi-lingual model training method, apparatus, electronic device and readable storage medium

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Beijing, China

Yu Sun