Emc IP Holding Company LLC

Emc Corporation

Beijing Baidu Netcom Science and Technology Co., Ltd.

Pivotal Software, Inc.

Beijing Baidu Netcom Science Technology Co., Ltd

Shengwen Yang

Caleb E Welton

Hai Qian

Rahul Iyer

Mingyang Dai

Xixuan Feng

Guangxin Yang

Xuhui Zhou

Wenwu Zhu

Yixuan Shi

Zixiang Liu

Bing Ren

A method and device for training a model based on federated learning are provided. The method includes: receiving a second original independent variable calculated value from a second data provider device; the second original independent variable calculated value being calculated by the second data provider device according to a second original independent variable and a second model parameter; calculating a dependent variable estimation value according to a first model parameter initial value of a first provider device, a first original independent variable of the first data provider device, and the second original independent variable calculated value; calculating a difference between a dependent variable of the first data provider device and the dependent variable estimation value; calculating a gradient of a loss function with respect to a first model parameter, according to the difference; and updating the first model parameter according to the gradient of the loss function with respect to the first model parameter.

Method and device for training a model based on federated learning

The present disclosure provides a method and an apparatus of recommending information based on a fused relationship network. The method includes: determining association relationships between an any node and other nodes in the fused relationship network based on at least one of a weight, interaction information and data source information of the interaction information of an edge; and recommending information to a user represented by the any node based on the association relationships. The present disclosure further provides an apparatus of recommending information based on a fused relationship network, and an electronic device and a storage medium.

Method and apparatus of recommending information based on fused relationship network, and device and medium

A network convergence method and device, an electronic apparatus, a storage medium are provided. The method includes: taking online information obtained at least from network data as first nodes, and constructing a first network with the first nodes based on an association among the online information, the online information including at least one account and at least one piece of network media information; taking offline information obtained at least from mobile communication data as second nodes, and constructing a second network with the second nodes based on an association among the offline information, the offline information including at least one terminal identifier and location information of a terminal corresponding to the at least one terminal identifier; and converging the first network and the second network by using an association between the account in the online information and the terminal identifier in the offline information, to construct a heterogeneous network.

Network convergence method and device, electronic apparatus, and storage medium

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for parallel time series analysis. One of the methods includes receiving a plurality of data elements. The plurality of data elements are partitioned into a plurality of chunks, wherein the plurality of chunks, including a first chunk and a plurality of additional chunks, have an ordering according to the data elements included in each chunk. Each chunk is assigned to a particular segment of a plurality of segments. A first iteration of an autoregressive integrated moving average is computed for each chunk assigned to each segment. A second iteration of the autoregressive integrated moving average is computed for each chunk assigned to each segment, wherein computing uses the result data for a corresponding preceding chunk in the first iteration. One or more additional iterations of the autoregressive integrated moving average are computed until stopping criteria has been satisfied.

Parallel time series analysis

Performing data analytics processing in the context of a large scale distributed system that includes a massively parallel processing (MPP) database and a distributed storage layer is disclosed. In various embodiments, a data analytics request is received. A plan is created to generate a response to the request. A corresponding portion of the plan is assigned to each of a plurality of distributed processing segments, including by invoking as indicated in the assignment one or more data analytical functions embedded in the processing segment.

Data analytics platform over parallel databases and distributed file systems

A general framework for cross-validation of any supervised learning algorithm on a distributed database comprises a multi-layer software architecture that implements training, prediction and metric functions in a C++ layer and iterates processing of different subsets of a data set with a plurality of different models in a Python layer. The best model is determined to be the one with the smallest average prediction error across all database segments.

General framework for cross-validation of machine learning algorithms using SQL on distributed systems

A framework for executing iterative grouped optimization algorithms such as machine learning and other analytic algorithms directly on unsorted data within a SQL data store without first redistributing the data comprises an architecture that provides C++ abstraction layers that include the algorithms over a SQL data store, and a higher Python abstraction layer that includes grouping and iteration controllers and call functionality to the C++ layer for invocation of the algorithms.

Framework for calculating grouped optimization algorithms within a distributed data store

Latent Dirichlet allocation (LDA) analysis on a dataset is performed on an MPP relational database by distributing subsets of said dataset to a plurality of segments of the MPP database, and performing LDA analysis in parallel on the respective subsets on the plurality of segments using Gibbs sampling. An object library on each segment provides executable objects of user defined functions that can be called by an SQL query when the query requires functionality provided by an object.

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Beijing, China

Shengwen Yang