TRAINING A NEURAL NETWORK USING STOCHASTIC WHITENING BATCH NORMALIZATION
A neural network system, comprising: instructions for implementing at least a SWBN layer in a neural network, and wherein the instructions perform operations comprising: during training of the neural network system on a plurality of batches of training data and for each of the plurality of batches:...
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creator | FASHANDI, Homa NEZHADARYA, Ehsan ZHANG, Shengdong LIU, Jiayi |
description | A neural network system, comprising: instructions for implementing at least a SWBN layer in a neural network, and wherein the instructions perform operations comprising: during training of the neural network system on a plurality of batches of training data and for each of the plurality of batches: obtaining a respective first layer output for each of the plurality of training data; determining a plurality of normalization statistics for the batch from the first layer outputs; generating a respective normalized output for each training data in the batch; updating the whitening matrix by a covariance matrix; performing stochastic whitening on the normalized components of each first layer output; transforming the whitened data for each training data; generating a respective SWBN layer output for each of the training data from the transformed whitened data for each training data in the batch; and providing the SWBN layer output. |
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determining a plurality of normalization statistics for the batch from the first layer outputs; generating a respective normalized output for each training data in the batch; updating the whitening matrix by a covariance matrix; performing stochastic whitening on the normalized components of each first layer output; transforming the whitened data for each training data; generating a respective SWBN layer output for each of the training data from the transformed whitened data for each training data in the batch; and providing the SWBN layer output.</description><language>eng ; fre ; ger</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2024</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=20240424&DB=EPODOC&CC=EP&NR=4128061A4$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,777,882,25545,76296</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240424&DB=EPODOC&CC=EP&NR=4128061A4$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>FASHANDI, Homa</creatorcontrib><creatorcontrib>NEZHADARYA, Ehsan</creatorcontrib><creatorcontrib>ZHANG, Shengdong</creatorcontrib><creatorcontrib>LIU, Jiayi</creatorcontrib><title>TRAINING A NEURAL NETWORK USING STOCHASTIC WHITENING BATCH NORMALIZATION</title><description>A neural network system, comprising: instructions for implementing at least a SWBN layer in a neural network, and wherein the instructions perform operations comprising: during training of the neural network system on a plurality of batches of training data and for each of the plurality of batches: obtaining a respective first layer output for each of the plurality of training data; determining a plurality of normalization statistics for the batch from the first layer outputs; generating a respective normalized output for each training data in the batch; updating the whitening matrix by a covariance matrix; performing stochastic whitening on the normalized components of each first layer output; transforming the whitened data for each training data; generating a respective SWBN layer output for each of the training data from the transformed whitened data for each training data in the batch; and providing the SWBN layer output.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZPAICXL09PP0c1dwVPBzDQ1y9AFSIeH-Qd4KocEg4eAQf2cPx-AQT2eFcA_PEFewWifHEGcPBT__IF9HH88oxxBPfz8eBta0xJziVF4ozc2g4OYKVKWbWpAfn1pckJicmpdaEu8aYGJoZGFgZuhoYkyEEgDUOyzI</recordid><startdate>20240424</startdate><enddate>20240424</enddate><creator>FASHANDI, Homa</creator><creator>NEZHADARYA, Ehsan</creator><creator>ZHANG, Shengdong</creator><creator>LIU, Jiayi</creator><scope>EVB</scope></search><sort><creationdate>20240424</creationdate><title>TRAINING A NEURAL NETWORK USING STOCHASTIC WHITENING BATCH NORMALIZATION</title><author>FASHANDI, Homa ; 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determining a plurality of normalization statistics for the batch from the first layer outputs; generating a respective normalized output for each training data in the batch; updating the whitening matrix by a covariance matrix; performing stochastic whitening on the normalized components of each first layer output; transforming the whitened data for each training data; generating a respective SWBN layer output for each of the training data from the transformed whitened data for each training data in the batch; and providing the SWBN layer output.</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
title | TRAINING A NEURAL NETWORK USING STOCHASTIC WHITENING BATCH NORMALIZATION |
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