Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
Optimization as a Model for Few-Shot Learning -...
Search
Hokuto Kagaya
June 16, 2017
Science
1.4k
2
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
Optimization as a Model for Few-Shot Learning - ICLR 2017 reading seminar
Hokuto Kagaya
June 16, 2017
More Decks by Hokuto Kagaya
See All by Hokuto Kagaya
LINE Bot ✕ Chainer - low-carb recipe bot -
hokkun
0
1.3k
Other Decks in Science
See All in Science
[Relay webinaire eBIS actu n°16] Amélioration de la survie des JB avec la génétique - Introduction
institutdelelevage
PRO
0
180
[Webinaire InnOvin] Coup de Chaud : Comment préserver la santé des animaux
institutdelelevage
PRO
0
220
Inside the Mind of an LLM
baggiponte
0
360
GKE上でオセロの強化学習やってみた
akasan
0
110
[TMLR 2026, Featured Certification] Double Bounded α-Divergence Optimization for Density Estimation
gkazunii
1
130
生成AI・プレプリント時代における 研究成果公開の再設計 ― トップカンファレンス文化はどこへ向かうのか / Redesigning the Dissemination of Research Outputs in the Age of Generative AI and Preprints — Where Is the Top-Conference Culture Heading?
ykiyota
0
30k
1. CPC理論の展開と集合的知能モデル(JSAI2026 KS-27 集合的予測符号化と新たな知性の時代)
hayashiyus884
1
410
機械学習 - DBSCAN
trycycle
PRO
0
2.1k
ゲームと人工知能
miyayou
0
250
ssmonline #51 ヤマサキ春のサメ祭り 2026 / ssmjp Yamasaki Spring JAWS Festival 2026
naospon
1
160
Build your own LLM, Live, with MicroGPT
ianozsvald
0
150
Leitner Inauguration Lecture Chalmers University of Technology
xleitix
0
370
Featured
See All Featured
Mozcon NYC 2025: Stop Losing SEO Traffic
samtorres
1
570
エンジニアに許された特別な時間の終わり
watany
109
250k
B2B Lead Gen: Tactics, Traps & Triumph
marketingsoph
0
260
Digital Ethics as a Driver of Design Innovation
axbom
PRO
1
450
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
52
6.1k
Building Applications with DynamoDB
mza
96
7.2k
A Modern Web Designer's Workflow
chriscoyier
699
190k
Groundhog Day: Seeking Process in Gaming for Health
codingconduct
0
390
How to Build an AI Search Optimization Roadmap - Criteria and Steps to Take #SEOIRL
aleyda
1
2.2k
Are puppies a ranking factor?
jonoalderson
2
4k
Done Done
chrislema
187
17k
The agentic SEO stack - context over prompts
schlessera
0
970
Transcript
!%F/" 4IJCVZB)JLBSJF )PLVUP ,BHBZB !@IPLLVO@ 0QUJNJ[BUJPOBTB.PEFM GPS'FX4IPU-FBSOJOH
5-%3 • 1VSQPTF • #FUUFSJOGFSFODFGPSGFXTIPUPOFTIPUMFBSOJOH QSPCMFN • .FUIPE • .FUBMFBSOJOHCBTFEPO-45.PGEFFQOFVSBM
OFUXPSL • 3FTVMU • $PNQFUJUJWFXJUIEFFQNFUSJDMFBSOJOHUFDIOJRVFT
#BDLHSPVOE • 8IZEFFQMFBSOJOHTVDDFFEFE • NBDIJOFQPXFS • BNPVOUPGEBUB • -BSHF%BUBTFU
• *NBHF/FU *NBHF • .JDSPTPGU$0$0$BQUJPOT *NBHF$BQUJPO • :PV5VCF. 7JEFP • 8JLJ5FYU 5FYU
#BDLHSPVOE • )PXFWFS *ONBOZGJFMET UPDPMMFDUBMBSHF BNPVOUPGUSBJOJOHTBNQMFTJT • EJGGJDVMU •
&Y'JOFHSBJOFESFDPHOJUJPO DBS CJSE GPPE • UJNFDPOTVNJOH • TDSBQJOH DSBXMJOH BOOPUBUJOHʜ • "DUVBMMZIVNBOCFJOHTDBOHFOFSBMJ[FVTJOH GFXTBNQMFTPGUBSHFUT
1SPCMFN1VSQPTF • )PXDBOXFBDRVJSFHFOFSBMJ[FENPEFMVTJOH GFXTBNQMFTBOEBTFUOVNCFSPGVQEBUFT • &YJTUFEHSBEJFOUCBTFEUSBJOJOHBMHPSJUIN 4(% "%".
"EB(MBE EPFTOPUGJUUIFQSPCMFNXJUIB TFUOVNCFSPGQBSBNFUFSVQEBUFT • *OPUIFSTJNQMFXPSET BVUIPSTXBOUUPGJOE HPPEJOJUJBMQBSBNFUFSTPG// • DG SFWJFXDPNNFOUTJUJTNVDICFUUFSUPCFBCMFUP GJOEBSDIJUFDUVSBMQBSBNFUFSTPG//
1SPCMFN1VSQPTF • )PX • .FUBMFBSOJOH • -FBSOJOHUPMFBSO5SBJOMFBSOFSJUTFMG • "WBSJFUZPGNFUBMFBSOJOH
• 5SBOTGFSMFBSOJOH • 6TFUIFFYQFSJFODFTPGEJGGFSFOUEPNBJO • 1PQVMBSJOUIFGJFMEPGJNBHFDMBTTJGJDBUJPO FTQFDJBMMZGPS GJOFHSBJOFEWJTVBMDMBTTJGJDBUJPO • &OTFNCMFDMBTTJGJFS • DPNCJOFNVMUJQMFDMBTTJGJFS 5IJTBSUJDMFJTWFSZHPPEUPVOEFSTUBOENFUBMFBSOJOH IUUQIUUQXXXTDIPMBSQFEJBPSHBSUJDMF.FUBMFBSOJOH
1SPQPTFENFUIPE • -45.CBTFENFUBMFBSOJOH
1SFSFRVJTJUFT • 8IBUJT-45. • -POHUJNF4IPSU5FSN.FNPSZ • ࣌ܥྻΛѻ͍͍ͨɺͰޡ͕ࠩൃࢄফࣦͪ͠Ό͏ • աڈͷσʔλͷॏΈΛ̍ʹͯ͠Εͳ͍Α͏ʹ্ͨ͠ Ͱɺબతʹೖྗग़ྗΛߦ͏Α͏ʹͨ͠
b • ͔͠͠ٸܹͳঢ়گͷมԽʢʁʣʹରԠͰ͖ͳ͔ͬͨͷ Ͱɺ٫ήʔτΛઃஔ͢Δ͜ͱͰબతʹաڈͷσʔ λͷهԱΛফڈͰ͖ΔΑ͏ʹͨ͠ ` • ࢀߟʢຊޠʣ • IUUQRJJUBDPNU@4JHOVMMJUFNTCCFCGEC
%BUB4FQBSBUJPO • NFUBUSBJOEBUBTFU • NFUBUFTUEBUBTFU • NFUBTBNQMF UBSHFU USBJOJOH
TBNQMFT UBSHFU UFTUJOH TBNQMFT POFNFUBTBNQMF BLBFQJTPEF
1SPQPTFE.FUIPE " = "$% + )*+, ℒ" " =
" ⨀"$% + " ⨀̅" XIFSF " = (6 7 ? + b9 ) " = ; 7 ? + b< XIFSF ? DVSSFOUHSBEJFOUT DVSSFOUMPTT QSFWJPVT QSFWJPVTJUTFMG , /PSNBM4(% .FUBQIPS OPUDPOTUBOU UP FTDBQFGSPNCBE MPDBMPQUJNB
1SPQPTFE.FUIPE ←meta learner‘s iteration ←learner‘s iteration (meta) loss
value is computed by final state of LSTM (= parameters of target model) and "?@"’s data and labels.
1SPQPTFE.FUIPE • 'SPNBVUIPS`TTMJEF
1SPQPTFE.FUIPE • 8IBUXJMMCFJNQSPWFEHSBEVBMMZ • 'JSTU-45.QBSBNFUFS BLBNFUBMFBSOFS QBSBNFUFST • UIBUJT
zIPXTIPVMEXFVQEBUFUBSHFUNPEFMT z • 4FDPOE-45.TUBUFT PVUQVUT • 'JOBMA JTTIBSFEBNPOHFBDICBUDI TPMFBSOJOHQSPDFFET SBQJEMZUIBOLTGPSHPPEJOJUJBMJ[BUJPO
0UIFS5PQJDT • DPPSEJOBUFXJTF-45. • 1SFQSPDFTTJOHUP-45.JOQVUT • BCPVUCPUIUPQJDT TFF<"OESZDIPXJD[ /*14> QSFQSPDFTTJOHJTJOBQQFOEJY
• BEKVTUUIFTDBMJOHPGHSBEJFOUTBOEMPTTFT • TFQBSBUFJOGPPGNBHOJUVEFBOETJHO • #BUDIOPSNBMJ[BUJPO • BWPJEzEBUBTFUz FQJTPEF MFWFMMFBLBHFPG JOGPSNBUJPO • 3FMBUFEXPSLNFUSJDMFBSOJOH • FY4JBNFTFOFUXPSL
&WBMVBUJPO.FUIPE • #BTFMJOFOFBSFTUOFJHICPS • NFUBUSBJOUSBJOOFVSBMOFUXPSLVTJOHBMMTBNQMF • NFUBUFTUUSBJOJOHTBNQMFΛ//ʹͿͪ͜Μͩ݁Ռͱ UFTUJOHTBNQMFͷͦΕΛൺֱ • #BTFMJOFGJOFUVOF
• NFUBUSBJOʹՃ͑ͯɺ NFUBWBMJEBUJPO EBUBTFU Λ IZQFS QBSBNFUFSͷ୳ࡧʹ͍ɺ ͷ OFUXPSLΛ GJOFUVOF ͢Δ • #BTFMJOF.BUDIJOHOFUXPSL • ڑֶशͷ405"
&WBMVBUJPO3FTVMU
7JTVBMJ[BUJPOBOE*OTJHIU
7JTVBMJ[BUJPOBOE*OTJHIU • JOQVUHBUFT • EJGGFSFOUBNPOHEBUBTFUT • NFUBMFBSOFSJTO`UTJNQMZMFBSOJOHBGJYFEPQUJNJ[BUJPO TUSBUFHZ • EJGGFSFOUBNPOHUBTLT
• NFUBMFBSOFSIBTVTFEEJGGFSFOUXBZTUPTPMWFFBDI TFUUJOH • GPSHFUHBUFT • TJNQMFEFDBZ • ݁ہ΄ͱΜͲ DPOTUBOU
$PODMVTJPO • 'PVOE-45.CBTFENPEFMUPMFBSOBMFBSOFS XIJDIJTJOTQJSFECZBNFUBQIPSCFUXFFO 4(%VQEBUFTBOE-45. • 5SBJONFUBMFBSOFSUPEJTDPWFS • (PPEJOJUJBMJ[BUJPOPGMFBSOFS
• (PPENFDIBOJTNGPSVQEBUJOHMFBSOFS`T QBSBNFUFST • DPNQFUJUJWFFYQFSJNFOUBMSFTVMUXJUI405" NFUSJDMFBSOJOHNFUIPET
'VUVSFXPSL • GFXTBNQMFTMPUTPGDMBTTFT • NPSFDIBMMFOHJOHTDFOBSJPT • GSPNSFWJFXDPNNFOU • JUJTNVDICFUUFSUPCFBCMFUPGJOEBSDIJUFDUVSBM QBSBNFUFSTPG//
ॴײ • USBOTGFSMFBSOJOHʹ͓͚ΔʮผυϝΠϯͷܦݧ Λ׆͔͢ʯͱ͍͏࡞ۀΛʮ࣌ܥྻͷֶशʯతʹଊ ͑ͯ -45.Ϟσϧͱֶͯ͠शͨ͠ɺͱ͍͏ͷ ࣗવʹࢥ͑ͨ • ͢Ͱʹ͋ͬͨൃʁ࣌ؒͳؔ͘࿈ݚڀ·ͰಡΈࠐΊ ͣɻɻ
• SFWJFXDPNNFOUʹ͋ͬͨɺߏͷ࠷దԽ·Ͱ Ͱ͖Δͱ͘͢͝Αͦ͞͏ͩͱࢥͬͨ • γϯϓϧͳϑΟϧλΛͨ͘͞ΜॏͶΔͱ͍͍ͱ͍͏ ͋Δ͕ɻɻ • ֶ෦࣌ DVEBDPOWOFU Λͬͯͨ͘͞ΜϋΠύύϥ ϝʔλΛࢼͨۤ͠࿑͕ોͬͨ
ଟΘ͔ͬͯͳ͍͜ͱ • ݁ہɺ͜ͷจͰॳΊͯΘ͔ͬͨͷͲ͜ʁ -45.Λ MFBSOJOHUPMFBSOʹͬͨͷଟॳ Ίͯ͡Όͳ͍ʁ • ྫ͑ "OESZDIPXJD[ `
ͰɺޯΛೖྗʹͯ͠ UBSHFUMFBSOFSͷ QBSBNFUFSVQEBUFT Λग़ྗ͢Δ -45.Λֶश • ύϥϝλͦͷͷΛग़ྗͯ͠Δͱ͜Ζʁ