Abstract: A method, an apparatus and a medium for optimizing allocation of switching resources in the polymorphic network. The method selects the ASIC switching chip, FPGA and PPK software switching on the polymorphic network element based on machine learning, and specifically comprises the following steps: manually pre-configuring, formulating basic rules for polymorphic software and hardware co-processing; offline learning, designing training configuration in the offline learning stage to capture different switching resource usage variables, running experiments to generate the original data of a training classifier, and using the generated performance indices to train the model offline; and online reasoning, obtaining switching resource allocation advises, and updating modality codes according to the switching resource allocation advises.

Abstract: Provided are a method for service status analysis, a server, and a storage medium. The method includes: for each vertex of multiple vertices in a graph database: reading out attribute data of the vertex, where the graph database is generated in advance according to a service description table, the vertex represents a service, and the attribute data of the vertex includes at least one service attribute of the service represented by the vertex; and according to the attribute data of the vertex and attribute data of each of multiple related vertices, determining a service status level of the service represented by the vertex, where each of the multiple related vertices has a propagation relationship with the vertex; and according to the service status level of each of the services represented by the vertices, analyzing a service propagation network.

Abstract: This application describes a model training method, applied to the field of artificial intelligence. The method includes a computing core of a first processor obtains an embedding used for model training, and writes an updated embedding to a first memory of the first processor instead of transferring the updated embedding to a second processor after model training is completed. In this application, after updating an embedding, the first processor saves the updated embedding to the first memory of the first processor. Without needing to wait for the second processor to complete a process of transferring a second target embedding to a GPU, the first processor may directly obtain the updated embedding and perform model training of a next round based on the updated embedding, provided that the first processor may obtain a latest updated embedding.

Abstract: Embodiments of this application provide a data processing method and apparatus to better learn a vector representation value of each feature value in a continuous feature. The method specifically includes: The data processing apparatus obtains the continuous feature from sample data, and then performs discretization processing on the continuous feature by using a discretization model, to obtain N discretization probabilities corresponding to the continuous feature. The N discretization probabilities correspond to N preset meta-embeddings, and N is an integer greater than 1. Finally, the data processing apparatus determines a vector representation value of the continuous feature based on the N discretization probabilities and the N meta-embeddings.

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: System and method for training a recommender system (RS). The RS is configured to make recommendations in respect of a bipartite graph that comprises a plurality of user nodes, a plurality of item nodes, and an observed graph topology that defines edges connecting at least some of the user nodes to some of the item nodes, the RS including an existing graph neural network (GNN) model configured by an existing set of parameters. The method includes: applying a loss function to compute an updated set of parameters for an updated GNN model that is trained with a new graph using the first set of parameters as initialization parameters, the loss function being configured to distil knowledge based on node embeddings generated by the existing GNN model in respect of an existing graph, wherein the new graph includes a plurality of user nodes and a plurality of item nodes that are also included in the existing graph; and replacing the existing GNN model of the RS with the updated GNN model.

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

Abstract: Provided are a method for service status analysis, a server, and a storage medium. The method includes: for each vertex of multiple vertices in a graph database: reading out attribute data of the vertex, where the graph database is generated in advance according to a service description table, the vertex represents a service, and the attribute data of the vertex includes at least one service attribute of the service represented by the vertex; and according to the attribute data of the vertex and attribute data of each of multiple related vertices, determining a service status level of the service represented by the vertex, where each of the multiple related vertices has a propagation relationship with the vertex; and according to the service status level of each of the services represented by the vertices, analyzing a service propagation network.

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: A recommendation model training method, a selection probability prediction method, and an apparatus are provided. The training method includes obtaining a training sample, where the training sample includes a sample user behavior log, position information of a sample recommended object, and a sample label. The training method further includes performing joint training on a position aware model and a recommendation model by the training sample, to obtain a trained recommendation model, where the position aware model predicts probabilities that a user pays attention to a target recommended object when the target recommended object is at different positions, and the recommendation model predicts, when the user pays attention to the target recommended object, a probability that the user selects the target recommended object.

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: 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.