Beijing Baidu Netcom Science and Technology Co., Ltd.

Google Inc.

Microsoft Technology Licensing, LLC

Beijing Baidu Netcom Service and Technology Co., Ltd.

Faen Zhang

Qian Wang

Kai Zhou

Yuanhao Xiao

Dongze Xu

Kun Liu

Lan Zhe Liu

Jin Tang

Tianhan Xu

Jiangliang Guo

Shiming Yin

Jiayuan Sun

Feng Wang

Yuan Liu

Minghao Liu

Siunie A Sutjahjo

Yong Ren

Guanfeng Li

Yong Zhang

Yanfeng Guo

Yang Liang

Xu Liang Li

Jiabing Leng

Yawei Wen

Kai Wang

Yuan Gao

Jiaxing Wang

Edward Y Chang

Hua Chai

Shuchang Zhou

Amit Behal

Keith Patrick Golden

Fei Wu

Yulin Xu

Jingbo Huang

Ben Hutchinson

Shuangshuang Qiao

Siyu An

Xiangyue Lin

Shan He

Lizhi Wu

Ziye Shi

Alexander Oliver Marks

Haishou Ma

Hang Jiang

Yongqiang Huang

Kaihua Song

The present disclosure discloses a method and apparatus for scheduling a resource for a deep learning framework. The method can comprise: querying statuses of all deep learning job objects from a Kubernetes platform at a predetermined interval; and submitting, in response to finding from the queried deep learning job objects a deep learning job object having a status conforming to a resource request submission status, a resource request to the Kubernetes platform to schedule a physical machine where the Kubernetes platform is located to initiate a deep learning training task. The method can completely automate the allocation and release on the resource of the deep learning training task.

Method and apparatus for scheduling resource for deep learning framework

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.

Method and apparatus for updating deep learning model

A method, device, apparatus and computer-readable storage medium for mining an enterprise relationship. The method can include acquiring Internet data of multiple types, identifying enterprise names from the Internet data by an enterprise name identification model, performing data mining to the Internet data in a frequent item mining algorithm to obtain relational degrees among the enterprise names, and according to the relational degrees among the enterprise names, extracting the enterprise relationship from the Internet data by an extractor. A deeper and more comprehensive enterprise relationship can be obtained by performing data mining to Internet data of multiple types by using an enterprise name recognition model. By using the frequent items mining algorithm, enterprises among which there are higher relational degrees can further be obtained, so as to obtain a more accurate enterprise relationship.

Method and device for mining an enterprise relationship

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.

Intelligent big data system, and method and apparatus for providing intelligent big data service

Embodiments of the present disclosure disclose a method and apparatus for recommending a video. A specific implementation of the method includes: finding a recommended video corresponding to a target video from all candidate videos based on similarities of content characteristics of the videos, the target video being a video to be played on a terminal of a user; and sending play information of the recommended video corresponding to the target video to the terminal of the user. The method finds a video similar on video content to the target video that the user desires to view based on the content characteristic of the video, and recommends the video similar on video content to the target video that the user desires to view to the user.

Method and apparatus for recommending video

A method and an apparatus for inspecting a corrosion defect of a ladle are provided. The method includes: acquiring images from various angles using an image acquisition apparatus inside a to-be-inspected ladle; and inputting the acquired images into a defect inspection system to obtain a label representing a defect category, the defect inspection system including: a deep convolutional neural network that predicts a category of a corrosion defect of the ladle included in the images based on the input images. This method has the advantages of high safety, high accuracy and high real-time performance in inspecting the ladle status.

Method and apparatus for inspecting corrosion defect of ladle

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.

Integrated facial recognition method and system

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.

Deep learning assignment processing method and apparatus, device and storage medium

A method, apparatus and system for detecting structural variations is provided. A management apparatus divides a test sequence according to loci of chromosomes to obtain at least two portions of detection tasks, sends respective detection tasks to respective detection apparatuses and activates the respective detection tasks; detects detection task completion situations of detection apparatuses and determines whether the number of uncompleted tasks is reduced to a preset proportion threshold of a total number of tasks; when the number is reduced to a preset proportion threshold of the total number of tasks, the management apparatus sends, to detection apparatuses that have not yet completed detection tasks, an instruction message to kill uncompleted detection tasks; the management apparatus further divides the uncompleted detection tasks into at least two portions, sends respective detection tasks to respective detection apparatuses, and activates said respective detection apparatuses to continue to perform detection of structural variations.

Method, apparatus and system for detecting structural variations

The application provides a method and a device for detecting a defect in a steel plate, as well as an apparatus and a server therefor. The method for detecting a defect in a steel plate comprises: receiving image data of the steel plate, and generating a defect detection request according to the image data; monitoring computing loads of a plurality of servers, and sending the image data and the defect detection request to a first server; receiving, from the first server, a detection result obtained by calculating the image data using the detection model; and operating according to the detection result, wherein the detection result comprises a selected one of a pass result and a defect result. With the proposed method, the position and the classification of the at least one defect can be obtained, so that the detection accuracy is improved.

Method and device for detecting a defect in a steel plate, as well as apparatus and server therefor

This disclosure describes a user interface (UI) conceptual model and a data model for quickly and easily developing and managing a GUI. The present methods and systems described herein provide for defining one or more UI elements in the UI conceptual model. The one or more UI elements may further reference one or more model objects in the data model. The one or more model objects may include one or more tasks, which may each be mapped to one or more commands for performing each task, or one or more data items, which may each be mapped to rules or conditions for transforming data. According to embodiments, graphics or functionality of a GUI may be easily and efficiently modified by adding, deleting, or modifying UI elements in the UI conceptual model or by adding, deleting, or modifying referenced model objects in the data model, thereby reducing management and development costs.

Data-driven schema for describing and executing management tasks in a graphical user interface

The present disclosure provides a method and apparatus for scheduling a cloud server. A specific implementation mode of the method comprises: monitoring whether current time is in a first pre-set time period; in response to the monitoring that the current time is in the first pre-set time period, scheduling a cloud server in a first cloud server cluster having a running state being an idle state, as a target cloud server, to a second cloud server cluster, so that the target cloud server executes a task obtained by the second cloud server cluster; monitoring whether the current time is in a second pre-set time period; in response to the monitoring that the current time is in the second pre-set time period, rescheduling the target cloud server to the first cloud server cluster, so that the target cloud server executes a task obtained by the first cloud server cluster.

Method and apparatus for scheduling cloud server

A method and system for providing an indoor map representation are provided herein. Aspects of this disclosure provide a representation of an indoor map environment whereby navigable areas are represented by a series of convex polygons in adjacency relationships with one another. Image analysis may be performed on a floor plan image to identify navigable areas and to determine the boundaries of the polygons, such as by assigning one or more colors to navigable space and one or more colors to non-navigable space. Route-finding operations may be performed using the adjacency relationships to find a path from a source area to a destination area.

Navigating using an indoor map representation

A system for automatically generating entity collections comprises a data graph including entities connected by edges and instructions that cause the computer system to determine a set of entities from the data graph and to determine a set of constraints that has a quantity of constraints. A constraint in the set represents a path in the data graph shared by at least two of the entities in the set of entities. The instructions also cause the computer system to generate candidate collection definitions from combinations of the constraints, where each candidate collection definition identifies at least one constraint and no more than the quantity of constraints. The instructions also cause the computer system to determine an information gain for at least some of the candidate collection definitions, and store at least one candidate collection definition that has an information gain that meets a threshold as a candidate collection.

Automatic definition of entity collections

Growing community of inventors

Beijing, China

Faen Zhang