公开(公告)号：US20170220949A1

公开(公告)日：2017-08-03

申请号：US15009968

申请日：2016-01-29

Applicant: Yahoo! Inc.

Inventor： Andrew Feng ,  Jun Shi ,  Mridul Jain ,  Peter Cnudde

IPC: G06N99/00

CPC classification number: G06N20/00

Abstract: The present teaching relates to distributed deep machine learning on a cluster. In one example, a request is received for estimating one or more parameters associated with a machine learning model on a cluster including a plurality of nodes. A set of data is obtained to be used for estimating the one or more parameters. The set of data is divided into a plurality of sub-sets of data, each of which corresponds to one of the plurality of nodes. Each sub-set of data is allocated to a corresponding node for estimating values of the one or more parameters based on the sub-set of data. Estimated values of the one or more parameters obtained based on a corresponding sub-set of data allocated to the node, are received from each of the plurality of nodes. The one or more parameters of the machine learning model are estimated based on the estimated values of the one or more parameters generated by at least some of the plurality of nodes.

公开(公告)号：US20170300828A1

公开(公告)日：2017-10-19

申请号：US15098415

申请日：2016-04-14

Applicant: Yahoo! Inc.

Inventor： Andrew Feng ,  Erik Ordentlich ,  Lee Yang ,  Peter Cnudde

IPC: G06N99/00 ,  G06N5/04 ,  G06N7/00 ,  H04L29/08

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.