MICROTRAINING FOR ITERATIVE FEW-SHOT REFINEMENT OF A NEURAL NETWORK
The disclosed microtraining techniques improve accuracy of trained neural networks by performing iterative refinement at low learning rates using a relatively short series microtraining steps. A neural network training framework receives the trained neural network along with a second training datase...
Gespeichert in:
Hauptverfasser: | , , , , |
---|---|
Format: | Patent |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | Lefohn, Aaron Eliot Xu, Yinghao Edelsten, Andrew Leighton Patney, Anjul Rowlett, Brandon Lee |
description | The disclosed microtraining techniques improve accuracy of trained neural networks by performing iterative refinement at low learning rates using a relatively short series microtraining steps. A neural network training framework receives the trained neural network along with a second training dataset and set of hyperparameters. The neural network training framework produces a microtrained neural network by adjusting one or more weights of the trained neural network using a lower learning rate to facilitate incremental accuracy improvements without substantially altering the computational structure of the trained neural network. The microtrained neural network may be assessed for changes in accuracy and/or quality. Additional microtraining sessions may be performed on the microtrained neural network to further improve accuracy or quality. |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2021287096A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2021287096A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2021287096A13</originalsourceid><addsrcrecordid>eNrjZHD29XQO8g8JcvT08_RzV3DzD1LwDHENcgzxDHNVcHMN1w328A9RCHJ18_Rz9XX1C1Hwd1NwVPBzDQ1y9AFSIeH-Qd48DKxpiTnFqbxQmptB2c01xNlDN7UgPz61uCAxOTUvtSQ-NNjIwMjQyMLcwNLM0dCYOFUA7o8skw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>MICROTRAINING FOR ITERATIVE FEW-SHOT REFINEMENT OF A NEURAL NETWORK</title><source>esp@cenet</source><creator>Lefohn, Aaron Eliot ; Xu, Yinghao ; Edelsten, Andrew Leighton ; Patney, Anjul ; Rowlett, Brandon Lee</creator><creatorcontrib>Lefohn, Aaron Eliot ; Xu, Yinghao ; Edelsten, Andrew Leighton ; Patney, Anjul ; Rowlett, Brandon Lee</creatorcontrib><description>The disclosed microtraining techniques improve accuracy of trained neural networks by performing iterative refinement at low learning rates using a relatively short series microtraining steps. A neural network training framework receives the trained neural network along with a second training dataset and set of hyperparameters. The neural network training framework produces a microtrained neural network by adjusting one or more weights of the trained neural network using a lower learning rate to facilitate incremental accuracy improvements without substantially altering the computational structure of the trained neural network. The microtrained neural network may be assessed for changes in accuracy and/or quality. Additional microtraining sessions may be performed on the microtrained neural network to further improve accuracy or quality.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; PHYSICS</subject><creationdate>2021</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20210916&DB=EPODOC&CC=US&NR=2021287096A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20210916&DB=EPODOC&CC=US&NR=2021287096A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Lefohn, Aaron Eliot</creatorcontrib><creatorcontrib>Xu, Yinghao</creatorcontrib><creatorcontrib>Edelsten, Andrew Leighton</creatorcontrib><creatorcontrib>Patney, Anjul</creatorcontrib><creatorcontrib>Rowlett, Brandon Lee</creatorcontrib><title>MICROTRAINING FOR ITERATIVE FEW-SHOT REFINEMENT OF A NEURAL NETWORK</title><description>The disclosed microtraining techniques improve accuracy of trained neural networks by performing iterative refinement at low learning rates using a relatively short series microtraining steps. A neural network training framework receives the trained neural network along with a second training dataset and set of hyperparameters. The neural network training framework produces a microtrained neural network by adjusting one or more weights of the trained neural network using a lower learning rate to facilitate incremental accuracy improvements without substantially altering the computational structure of the trained neural network. The microtrained neural network may be assessed for changes in accuracy and/or quality. Additional microtraining sessions may be performed on the microtrained neural network to further improve accuracy or quality.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2021</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZHD29XQO8g8JcvT08_RzV3DzD1LwDHENcgzxDHNVcHMN1w328A9RCHJ18_Rz9XX1C1Hwd1NwVPBzDQ1y9AFSIeH-Qd48DKxpiTnFqbxQmptB2c01xNlDN7UgPz61uCAxOTUvtSQ-NNjIwMjQyMLcwNLM0dCYOFUA7o8skw</recordid><startdate>20210916</startdate><enddate>20210916</enddate><creator>Lefohn, Aaron Eliot</creator><creator>Xu, Yinghao</creator><creator>Edelsten, Andrew Leighton</creator><creator>Patney, Anjul</creator><creator>Rowlett, Brandon Lee</creator><scope>EVB</scope></search><sort><creationdate>20210916</creationdate><title>MICROTRAINING FOR ITERATIVE FEW-SHOT REFINEMENT OF A NEURAL NETWORK</title><author>Lefohn, Aaron Eliot ; Xu, Yinghao ; Edelsten, Andrew Leighton ; Patney, Anjul ; Rowlett, Brandon Lee</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2021287096A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2021</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>Lefohn, Aaron Eliot</creatorcontrib><creatorcontrib>Xu, Yinghao</creatorcontrib><creatorcontrib>Edelsten, Andrew Leighton</creatorcontrib><creatorcontrib>Patney, Anjul</creatorcontrib><creatorcontrib>Rowlett, Brandon Lee</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lefohn, Aaron Eliot</au><au>Xu, Yinghao</au><au>Edelsten, Andrew Leighton</au><au>Patney, Anjul</au><au>Rowlett, Brandon Lee</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>MICROTRAINING FOR ITERATIVE FEW-SHOT REFINEMENT OF A NEURAL NETWORK</title><date>2021-09-16</date><risdate>2021</risdate><abstract>The disclosed microtraining techniques improve accuracy of trained neural networks by performing iterative refinement at low learning rates using a relatively short series microtraining steps. A neural network training framework receives the trained neural network along with a second training dataset and set of hyperparameters. The neural network training framework produces a microtrained neural network by adjusting one or more weights of the trained neural network using a lower learning rate to facilitate incremental accuracy improvements without substantially altering the computational structure of the trained neural network. The microtrained neural network may be assessed for changes in accuracy and/or quality. Additional microtraining sessions may be performed on the microtrained neural network to further improve accuracy or quality.</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | eng |
recordid | cdi_epo_espacenet_US2021287096A1 |
source | esp@cenet |
subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS |
title | MICROTRAINING FOR ITERATIVE FEW-SHOT REFINEMENT OF A NEURAL NETWORK |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T00%3A55%3A58IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=Lefohn,%20Aaron%20Eliot&rft.date=2021-09-16&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2021287096A1%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |