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DTSTAMP:20230124T171522Z
LOCATION:C148
DTSTART;TZID=America/Chicago:20221113T134000
DTEND;TZID=America/Chicago:20221113T134400
UID:submissions.supercomputing.org_SC22_sess425_ws_whpc105@linklings.com
SUMMARY:Classification and Protection of Critical Weights in CNNs
DESCRIPTION:Workshop\n\nClassification and Protection of Critical Weights 
 in CNNs\n\nSchmedding\n\nConvolutional neural networks (CNNs) are being in
 corporated into many image-based tasks across a variety of domains. Some o
 f these tasks are real-world safety critical tasks such as object detectio
 n and lane line detection for self-driving cars. These applications have s
 trict safety requirements and must be able guarantee the reliable operatio
 n of the network. We propose a selective triplication of important parts o
 f the network determined via weight pruning methodologies in order to main
 tain a reliable CNN in environments that may be resource-limited.\n\nSessi
 on Format: Recorded\n\nTag: Diversity Equity Inclusion (DEI), Education an
 d Training and Outreach\n\nRegistration Category: Workshop Reg Pass
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