Abstract: A neural network construction method and system in the field of artificial intelligence, to construct a target neural network by replacing a part of basic units in an initial backbone network with placeholder modules, so that different target neural networks can be constructed based on different scenarios. The method may include obtaining an initial backbone network and a candidate set, replacing at least one basic unit in the initial backbone network with at least one placeholder module to obtain a to-be-determined network, performing sampling based on the candidate set to obtain information about at least one sampling structure, and obtaining a network model based on the to-be-determined network and the information about the at least one sampling structure. The information about the at least one sampling structure may be used for determining a structure of the at least one placeholder module.

Abstract: The present disclosure relates to neural network optimization methods and apparatuses in the field of artificial intelligence. One example method includes sampling preset hyperparameter search space to obtain multiple hyperparameter combinations. Multiple iterative evaluations are performed on the multiple hyperparameter combinations to obtain multiple performance results of each hyperparameter combination. Any iterative evaluation comprises obtaining at least one performance result of each hyperparameter combination, and if a hyperparameter combination meets a first preset condition, re-evaluating the hyperparameter combination to obtain a re-evaluated performance result of the hyperparameter combination. An optimal hyperparameter combination is determined. If the optimal hyperparameter combination does not meet a second preset condition, a preset model is updated, based on the multiple performance results of each hyperparameter combination, for next sampling.

Abstract: Example prediction methods and apparatus are described. One example includes sending a first model parameter and a second model parameter by a server to a plurality of terminals. The first model parameter and the second model parameter are adapted to a prediction model of the terminal. The server receives a first prediction loss sent by at least one of the plurality of terminals. A first prediction loss sent by each of the at least one terminal is calculated by the terminal based on the prediction model that uses the first model parameter and the second model parameter. The server updates the first model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated first model parameter. The server updates the second model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated second model parameter.

Abstract: This application relates to the field of artificial intelligence.

Abstract: A data processing method related to the field of artificial intelligence includes adding an architecture parameter to each feature interaction item in a first model, to obtain a second model, where the first model is a factorization machine (FM)-based model, and the architecture parameter represents importance of a corresponding feature interaction item; performing optimization on architecture parameters in the second model to obtain the optimized architecture parameters; and obtaining, based on the optimized architecture parameters and the first model or the second model, a third model through feature interaction item deletion.

Abstract: A processing apparatus includes a collection module and a training module, the training module includes a backbone network and a region proposal network (RPN) layer, the backbone network is connected to the RPN layer, and the RPN layer includes a class activation map (CAM) unit. The collection module is configured to obtain an image, where the image includes an image with an instance-level label and an image with an image-level label. The backbone network is used to output a feature map of the image based on the image obtained by the collection module.

Abstract: A neural network search method, apparatus, and device are provided, and relate to the field of artificial intelligence technologies, and specifically, to the field of automatic machine learning technologies. The method includes: A computing device obtains a dataset and N neural networks (S602), where N is a positive integer; and performs K evolutions on the N neural networks to obtain neural network obtained through the Kth evolution, where K is a positive integer (S604). In a process of each evolution, a network structure of a neural network obtained in previous evolution is mutated; and a candidate neural network is selected, based on a partially ordered hypothesis, from networks obtained through mutation.

Abstract: This application provides a method and related apparatus for determining a neural network in the field of artificial intelligence. The method includes: obtaining a plurality of initial search spaces; determining M candidate neural networks based on the plurality of initial search spaces, where the candidate neural network includes a plurality of candidate subnetworks, the plurality of candidate subnetworks belong to the plurality of initial search spaces, and any two of the plurality of candidate subnetworks belong to different initial search spaces; evaluating the M candidate neural networks to obtain M evaluation results; and determining N candidate neural networks from the M candidate neural networks based on the M evaluation results, and determining N first target neural networks based on the N candidate neural networks. According to the method and the related apparatus provided in this application, a combined neural network with relatively high performance can be obtained.

Abstract: The method includes: obtaining a plurality of pieces of feature data; automatically performing two different types of nonlinear combination processing operations on the plurality of pieces of feature data to obtain two groups of processed data, where the two groups of processed data include a group of higher-order data and a group of lower-order data, the higher-order data is related to a nonlinear combination of m pieces of feature data in the plurality of pieces of feature data, and the lower-order data is related to a nonlinear combination of n pieces of feature data in the plurality of pieces of feature data, where m?3, and m>n?2; and determining prediction data based on a plurality of pieces of target data, where the plurality of pieces of target data include the two groups of processed data.

Abstract: This application provides a neural architecture search method, an image processing method and apparatus, and a storage medium. The method includes: determining a search space and a plurality of structuring elements, stacking the plurality of structuring elements to obtain an initial neural architecture at a first stage, and optimizing the initial neural architecture at the first stage to be convergent; and after an initial neural architecture optimized at the first stage is obtained, optimizing the initial neural architecture at a second stage to be convergent, to obtain optimized structuring elements, and building a target neural network based on the optimized structuring elements. Each edge of the initial neural architecture at the first stage and each edge of the initial neural architecture at the second stage correspond to a mixed operator including one type of operations and a mixed operator including a plurality of types of operations respectively.

Abstract: The method includes: obtaining a plurality of pieces of feature data; automatically performing two different types of nonlinear combination processing operations on the plurality of pieces of feature data to obtain two groups of processed data, where the two groups of processed data include a group of higher-order data and a group of lower-order data, the higher-order data is related to a nonlinear combination of m pieces of feature data in the plurality of pieces of feature data, and the lower-order data is related to a nonlinear combination of n pieces of feature data in the plurality of pieces of feature data, where m?3, and m>n?2; and determining prediction data based on a plurality of pieces of target data, where the plurality of pieces of target data include the two groups of processed data.

Abstract: This application relates to an image recognition technology in the artificial intelligence field, and provides an image classification method and apparatus, and an image classification model training method and apparatus. This application relates to the artificial intelligence field, and more specifically, to the computer vision field. The method includes: obtaining a to-be-processed image; and classifying the to-be-processed image based on a preset global class feature, to obtain a classification result of the to-be-processed image. The global class feature includes a plurality of class features obtained through training based on a plurality of training images in a training set. The plurality of class features in the global class feature are used to indicate visual features of all classes in the training set.

Abstract: This application provides an object detection method and apparatus. This application relates to the field of artificial intelligence, and specifically, to the field of computer vision. The method includes: obtaining a to-be-detected image; performing convolution processing on the to-be-detected image to obtain an initial image feature of a to-be-detected object in the to-be-detected image; determining an enhanced image feature of the to-be-detected object based on knowledge graph information; and determining a candidate frame and a classification of the to-be-detected object based on the initial image feature and the enhanced image feature of the to-be-detected object. The enhanced image feature indicates semantic information of a different object category corresponding to another object associated with the to-be-detected object. Therefore, in this application, an effect of the object detection method can be improved.

Abstract: An image classification method, a neural network training method, and an apparatus are provided, and relate to the field of artificial intelligence, and specifically, to the field of computer vision. The image classification method includes: obtaining a to-be-processed image; and obtaining a classification result of the to-be-processed image based on a pre-trained neural network model, where the classification result includes a class or a superclass to which the to-be-processed image belongs. When the neural network model is trained, not only labels of a plurality of training images but also class hierarchy information of the plurality of training images is used. That is, more abundant information of the training images is used. Therefore, images can be better classified.

Abstract: The method of the present disclosure includes: after a graph partitioning apparatus extracts an edge, first determining whether an aggregation degree between a currently extracted edge and an allocated edge in a first device satisfies a preset condition; then, when the preset condition is satisfied, determining whether a quantity of allocated edges stored in the first device is less than a first preset threshold; and allocating the currently extracted edge to the first device when the quantity is less than the first preset threshold. In this way, an aggregation degree between allocated edges in each device is relatively high and each device has relatively balanced load. When an edge changes and an edge associated with the particular edge needs to be synchronized, a relatively small quantity of devices need to perform synchronization and update, so that costs of communication between devices are reduced, and distributed graph computing efficiency is improved.

Abstract: The present invention relates to a mutant p-hydroxyphenylpyruvate dioxygenase (HPPD) protein or a bioactive fragment thereof and an isolated polynucleotide comprising a nucleic acid sequence encoding the protein or fragment thereof, wherein the mutant p-hydroxyphenylpyruvate dioxygenase (HPPD) protein or a bioactive fragment thereof retains or enhances the property of catalyzing the conversion of p-hydroxyphenylpyruvate (HPP) to homogentisate and is significantly less sensitive to HPPD-inhibiting herbicides than a wild-type HPPD. The present invention also relates to a nucleic acid construct, an expression vector and a host cell comprising the polynucleotide, as well as to a method for producing a plant that has the property of catalyzing the conversion of p-hydroxyphenylpyruvate (HPP) to homogentisate and significantly reduced sensitivity to HPPD-inhibiting herbicides.

Abstract: A self-driving method and a related apparatus, the method including determining, by a self-driving apparatus, a task feature vector of a self-driving task according to M groups of historical paths of the self-driving task, where the task feature vector is a vector representing features of the self-driving task, and where M is an integer greater than 0, determining, by the self-driving apparatus, according to the task feature vector and a status vector, a target driving operation that needs to be performed, where the status vector indicates a driving status of the self-driving apparatus, and performing, by the self-driving apparatus, the target driving operation.

Abstract: The present application discloses a method and an apparatus for processing a development machine operation task, a device and a storage medium, which relates to the field of deep learning of artificial intelligence. The specific implementation solution is: receiving a task creating request initiated by a client; generating, according to the task creating request, the development machine operation task; allocating a target graphics processing unit (GPU) required for executing the development machine operation task for the development machine operation task; and sending a development machine operation task request to a master node in cluster nodes, where the task request is used to request executing the development machine operation task on the target GPU.

Abstract: A data processing method, includes receiving a data flow; generating a triplet set according to the data flow, where each triplet in the set includes three items, the first item is a first element in the data flow, the second item includes a first time point at which the first element appears in the data flow and a first quantity of times that corresponds to the first time point, and the third item includes a second time point at which the first element appears in the data flow and a second quantity of times that corresponds to the second time point; and performing data processing on the data flow according to the triplet set. In the embodiments of the present application, the triplet set may be generated based on the data flow.

Abstract: A processing entity generates a model for estimating scene illumination colour for a source image captured by a camera The processing entity acquires a set of images, captured by a respective camera, the set of images as a whole including images captured by multiple cameras; forms a set of tasks by assigning each image of the images set to a respective task such that images in the same task have in common that a the images are in a predetermined range; trains model parameters by repeatedly: selecting at least one of the tasks, forming an interim set of model parameters based on a first subset of the images of that task, estimating the quality of the interim set of model parameters against a second subset of the images of that task and updating the parameters of the model based on the interim set of parameters and the estimated quality.