http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2020209860-A1

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filingDate 2019-04-11-04:00^^<http://www.w3.org/2001/XMLSchema#date>
inventor http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_7184c3b0486aca80e18a67fe8c1b7924
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_33c8f776de262e0df89b11f549348442
publicationDate 2020-10-15-04:00^^<http://www.w3.org/2001/XMLSchema#date>
publicationNumber WO-2020209860-A1
titleOfInvention Leveraging lagging gradients in machine-learning model training
abstract A computer-implemented method for distributed synchronous training of a neural network model includes detecting a plurality of gradient sets from a corresponding plurality of worker machines. Each of the worker machines generates a corresponding gradient set of the plurality of gradient sets in a current iteration of a training data set. Each gradient set of the plurality of gradient sets comprises a plurality of gradients. A lagging gradient set from another worker machine is detected. The lagging gradient set is generated by the another worker machine in a prior iteration of the training data set. Aggregated gradients are generated based on the plurality of gradient sets and the lagging gradient set. The neural network model is updated based on the aggregated gradients.
isCitedBy http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-116702885-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/EP-4009241-A1
priorityDate 2019-04-11-04:00^^<http://www.w3.org/2001/XMLSchema#date>
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