BEGIN:VCALENDAR PRODID:-//Microsoft Corporation//Outlook 16.0 MIMEDIR//EN VERSION:2.0 METHOD:REQUEST X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VTIMEZONE TZID:W. Europe Standard Time BEGIN:STANDARD DTSTART:16011028T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010325T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT ATTENDEE;CN="Markus Lange-Hegermann (markus.lange-hegermann@th-owl.de)";RSV P=TRUE:mailto:markus.lange-hegermann@th-owl.de CLASS:PUBLIC CREATED:20220328T094854Z DESCRIPTION:Title: Rethinking Architecture Selection in Differentiable NAS\ n \n\n \nAbstract:\nIn the domain of Neural Architecture Search (NAS)\, di fferentiable NAS remains as one of the most popular methods due to its sea rch simplicity and efficiency. This is achieved by combining the optimizat ion of model weights and the architecture parameters via a weight-sharing supernet through gradient-based algorithms. The selected largest architect ure parameters towards the end of the search operation is consequently use d to be the final architecture. However\, this is done using the implicit assumption that the values of network architecture parameters inherently r eflect the search operation strength. Surprisingly\, the architecture sele ction process has received very little research and attention\, whereas\, the supernet's optimization is researched in depth.\n \nIn this research p aper\, the authors provide different analysis to demonstrate that the magn itude of architecture parameters do not reflect the aforementioned inhrent assumption of how much this operation contributes to the supernet's conse quent performance. Rather\, they propose disturbance based alternative arc hitecture selection process which directly quantifies each operation's res ulting effect on the supernet.\n \nLinks:\nRethinking Architecture Selecti on in Differentiable NAS: https://arxiv.org/pdf/2108.04392.pdf \n \nSpeake rs: \nArjun Majumdar (TH OWL)\n \nFor questions or suggestions of topics\, feel free to contact markus.lange-hegermann@th-owl.de \n \n \n-- Den nachstehenden Text weder löschen noch ändern. -- \n \nTreten Sie Ihrem Webex-Meeting zum gegebenen Zeitpun kt hier bei. \n \n \nMeeting beitreten \n \nWeitere Methoden zum Beit reten: \n \nÜber den Meeting-Link beitreten \nhttps://th-owl.webex.com/th -owl/j.php?MTID=m8122370def638b734732e5b2f4441074 \n \n \nMit Meetin g-Kennnummer beitreten \nMeeting-Kennnummer (Zugriffscode): 2730 726 5649\ nMeeting-Passwort: nmY6rsGWX57 \n \nHier tippen\, um mit Mobilgerät beiz utreten (nur für Teilnehmer) \n+49-619-6781-9736\,\,27307265649## Germany Toll \n+49-619- 6781-9736\,\,27307265649## Germany Toll \n\nÜber Telefon beitreten \n+49-619-6781-9736 Ge rmany Toll \n+49-619-6781-9736 Germany Toll \nGlobale Einwahlnummern \n \nMit Videosystem\, Anwendung oder Skype for Business t eilnehmen\nWählen Sie 27307265649@webex.com \nSie können auch 62.109.219.4 wählen und Ihre Meeting-Nummer eingeben. \n \nWenn Sie ein Gastgeber sind\, klicken Sie hier \, um Gastgeberi nformationen anzuzeigen.\n \n \nBrauchen Sie Hilfe? Gehen Sie zu https://h elp.webex.com \n \n \n \n DTEND;TZID="W. Europe Standard Time":20220412T180000 DTSTAMP:20220328T080830Z DTSTART;TZID="W. Europe Standard Time":20220412T160000 LAST-MODIFIED:20220328T094854Z LOCATION:https://th-owl.webex.com/th-owl/j.php?MTID=m8122370def638b734732e5 b2f4441074 ORGANIZER;CN="Jörn Tebbe":mailto:joern.tebbe@th-owl.de PRIORITY:5 SEQUENCE:0 SUMMARY;LANGUAGE=de:MLRG Round 7: Rethinking Architecture Selection in Diff erentiable NAS TRANSP:OPAQUE UID:040000008200E00074C5B7101A82E0080000000060E8FE8E8A42D801000000000000000 0100000009DFE8FA0D4DE5946BA16F32C84E60D41 X-ALT-DESC;FMTTYPE=text/html:

Title: Rethinking Architecture Selection in Differentiable NAS

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cid:image001.jpg@01D8428B.C6036BF0

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Abstract< span lang=EN-US style='mso-ansi-language:EN-US'>:

In the do main of Neural Architecture Search (NAS)\, differentiable NAS remains as o ne of the most popular methods due to its search simplicity and efficiency . This is achieved by combining the optimization of model weights and the architecture parameters via a weight-sharing supernet through gradient-based algorithms. The selected largest architecture parameters towards the end of the search operation is consequently used t o be the final architecture. However\, this is done using the implicit ass umption that the values of network architecture parameters inherently refl ect the search operation strength. Surprisingly\, the architecture selecti on process has received very little research and attention\, whereas\, the supernet's optimization is researched in depth.

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In this research paper\, the author s provide different analysis to demonstrate that the magnitude of architec ture parameters do not reflect the aforementioned inhre nt assumption of how much this operation contributes to the supernet's consequent performance. Rather\, they propos e disturbance based alternative architecture selection process which direc tly quantifies each operation's resulting effect on the supernet.

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Links:< /span>

Rethinking Architecture Selection in Differentiable NAS: https://arxiv.org/pdf/2108.04392.pdf

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Speakers:

Arjun Majumdar (TH OWL)

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For questions or suggestions of topics\, feel free to contact markus.lange-hegermann@th-owl. de  \;

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-- Den nachstehenden Text weder löschen noch ändern. -- < o:p>

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Treten Sie Ihrem Webex-Meeting zum g egebenen Zeitpunkt hier bei.

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htt ps://th-owl.webex.com/th-owl/j.php?MTID=m8122370def638b734732e5b2f4441074< /span>

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