Graph Neural Network

KCS AI班 論文輪講会 #1-1

Transcript

1. (SBQI
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2. "CTUSBDU ଟ͘ͷֶश՝୊Ͱ͸ɺཁૉؒͷ๛෋ͳؔ܎৘ใΛؚΉάϥϑσʔλΛѻ͏ඞཁ͕͋Δɻ ྫ
   ෺ཧγεςϜͷϞσϧԽɺNPMFDVMBS
   fi OHFSQSJOUTͷֶश
   λϯύΫ࣭ͷΠϯλʔϑΣΠε༧ଌɺපؾͷ෼ྨ άϥϑσʔλ ֶशϞσϧ ग़ྗ (//͸ϊʔυؒͷNFTTBHF
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3. 5IF
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4. 5IF
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   B
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5. 1PPMJOH
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6. 5IF
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   B
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7. 5IF
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   B
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8. 4BNQMJOH
   0QFSBUPS (//ϞσϧͰ͸ɺ֤ϊʔυʹର͢ΔϝοηʔδΛྡ઀͔Βू໿͢ΔͨΊɺ ϊʔυ͕ଟ͚Ε͹ଟ͍΄Ͳࢦ਺ؔ਺తʹܭࢉྔ͕૿Ճ͢Δɻ neighbor explosion େن໛άϥϑͰ͸શͯͷۙ๣৘ใΛৗʹอଘͯ͠ॲཧ͢Δ͜ͱ͸Ͱ͖ͳ͍ αϯϓϦϯάΛߦ͏ԋࢉࢠΛߟ͑Δ
   ֤ϊʔυͷۙ๣͔ΒαϒηοτΛબ୒͠ɺྡ઀͢Δϊʔυͷ
   Node Sampling⋯ αΠζΛখ͘͢͞Δ (SBQI4"(&
   
9. 5IF
   HFOFSBM
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   B
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10. 5IF
    HFOFSBM
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    GPS
    B
    (//
    NPEFM
11. $POW3FDVSSFOU
    0QFSBUPS άϥϑ৴߸ ͷεϖΫτϧྖҬͰͷ৞ΈࠐΈΛ࣮ߦ͢Δɻ x Spectral approaches ℱ(x) = UT x άϥϑ৴߸ ΛάϥϑϑʔϦΤม׵ ʹΑͬͯεϖΫτϧྖҬʹม׵͠ɺ৞ΈࠐΈΛߦ͏ɻ x ℱ ℱ−1(x) = Ux ৞ΈࠐΈԋࢉࢠΛ ͱ͢ΔͱɺҎԼͷΑ͏ͳ৞ΈࠐΈ͕ߦΘΕΔɻ G Convolution theorem G(x) = ℱ−1(ℱ(G) ⊙ ℱ(x)) = U(UTg ⊙ UT x) ͜͜Ͱ ͸ਖ਼نԽάϥϑϥϓϥγΞϯ U L = IN − D−1 2 AD−1 2 ͷݻ༗ϕΫτϧͷߦྻͰ͋Γɺ Λຬͨ͢ɻ ͸άϥϑͷ࣍਺ߦྻ
    L = UΛUT D ͸άϥϑͷྡ઀ߦྻ
    A IUUQTCVJMEFSTCPYDPSQTBOTBODPNFOUSZ
12. $POW3FDVSSFOU
    0QFSBUPS IUUQTBJTDIPMBSUFDIBSUJDMFTHOO(//@SFWJFX
13. 5IF
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    B
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14. 5IF
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    B
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15. 4LJQQJOH
    $POOFDUJPO T(ht) = σ(Wt ht + bt ), ht+1 = ht+1 ⊙ T(ht) + ht(1 − T(ht)) . Ұൠతʹ//ͷ૚Λਂ͍ͯ͘͘͠ͱޯ഑ফࣦ໰୊౳͕ى͜Γɺֶश͕ਐΈͮΒ͘ͳΔɻ )JHIXBZ
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16. Ԡ༻ྫ