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What Can Neural Networks Reason About?
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Scatter Lab Inc.
May 29, 2020
Research
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What Can Neural Networks Reason About?
Scatter Lab Inc.
May 29, 2020
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Transcript
8IBUDBOOFVSBMOFUXPSL SFBTPOBCPVU ҳ࢚ળ .-4DJFOUJTU 1JOHQPOH
ݾର ݾର • Introduction: Reasoning • Algorithmic Alignment • Conclusion
*OUSPEVDUJPO3FBTPOJOH
8IBUJT3FBTPOJOH *OUSPEVDUJPO3FBTPOJOH • “୶ܻ”
8IBUJT3FBTPOJOH *OUSPEVDUJPO3FBTPOJOH • ୶ܻ: ঌҊ ח Ѫਵ۽ࠗఠ ঌ ޅೞח Ѫਸ
ࢎҊೣ • ীಘ ܻী যਃ -> ܻח یझ ࣻبীਃ -> ীಘ যו աۄী ਸө? • ࠁܳ ਸ ٸ, Ӓ ࠁ۽ࠗఠ ҙೞ ޅೠ Ѫী ೠ ࢜۽ ࠁܳ ب೧ղח স
8IBUJT3FBTPOJOH *OUSPEVDUJPO3FBTPOJOH • ࠁܳ ਸ ٸ, Ӓ ࠁ۽ࠗఠ ҙೞ ޅೠ
Ѫী ೠ ࢜۽ ࠁܳ ب೧ղח স • न҃ݎ ୶ܻ ޙઁ: ࠁ/ࣁ࢚ਸ ҳઑചೞҊ Ӓ ҳઑ۽ࠗఠ Ѿҗܳ ஏೞب۾ णदఇ • GNN, Neural Symbolic Programs (Semantic Parsing), Deep Sets
%FGJOJUJPOPG3FBTPOJOH *OUSPEVDUJPO3FBTPOJOH • ޛ s ∈ Sо যࢲ п
sܳ Xۄח ߭ఠ۽ അೡ ࣻ Ҋ о • ࢚ട {S1, S2, S3, …} ী ೧ࢲ ۄ߰ {y1, y2, y3, …} о ਸ ٸ • ࠁ ޅೠ ࢚ട Sী ೠ ۄ߰ yܳ بೞח ೣࣻ y=g(S)ܳ ח Ѫ ݾ
4USVDUVSFTGPS3FBTPOJOH *OUSPEVDUJPO3FBTPOJOH • Deep Sets • S = {ࡈр ҕ,
ۆ ҕ, ֢ۆ ҕ} ۄҊ о೧ࠁݶ, • ࢎۈ ੑীࢲח ࣽࢲо ࢚ҙ হ {ࡈр ҕ, ۆ ҕ, ֢ۆ ҕ} = {ۆ ҕ, ֢ۆ ҕ, ࡈр ҕ} • Permutation Invariant ೞѱ ೣࣻ gܳ ࢸ҅ೠ Ѫ Deep set • য۰ਕ ࠁ݅ Ӓր MLP MLPۄҊ ࢤп • যڃ ࣽࢲ۽ ৬ب ࢚ҙ হ
4USVDUVSFTGPS3FBTPOJOH *OUSPEVDUJPO3FBTPOJOH • GNN (Graph Neural Network) • Aggregate৬ Concatܳ
ഝਊೞৈ Graph ҳઑܳ Networkܳ അ
,JOEPG3FBTPOJOH *OUSPEVDUJPO3FBTPOJOH • ٜ ୶ܻ ޙઁܳ դب৬ ౠী ٮۄࢲ ֎
о۽ ࠙ܨೣ • ా҅ ਃড (Summary Statistics): য ੑ۱ਸ ‘ࣁח’ ޙઁ • द) ز ࢎী ݻ ѐ ա? Ҋনی ѐী যڃ Ѫ ݆ա? • ҙ҅ ࢲৌ (Relational Argmax): য ੑ۱ীࢲ ࢚ ҙ҅ܳ ٮח ޙઁ • द) о ݣܻ ڄযઉ ח ޛ ह যڃ ࢝ӭੋо? • ز ۽Ӓې߁ (Dynamic Programming): Ѿҗܳ ೞݴ ಹח ޙઁ • द) ೖࠁա, ઁੌ ૣ ӡ ӝ • NP-Hard Problem: ??? • द) য ী ೧ࢲ Ӓ 0غח ࠗ࠙ ਸ ਸ ࣻ ਸө?
,JOEPG3FBTPOJOH *OUSPEVDUJPO3FBTPOJOH • ٜ ୶ܻ ޙઁܳ դب৬ ౠী ٮۄࢲ ֎
о۽ ࠙ܨೣ • ా҅ ਃড (Summary Statistics): য ੑ۱ਸ ‘ࣁח’ ޙઁ • द) ز ࢎী ݻ ѐ ա? Ҋনی ѐী যڃ Ѫ ݆ա? • ҙ҅ ࢲৌ (Relational Argmax): য ੑ۱ীࢲ ࢚ ҙ҅ܳ ٮח ޙઁ • द) о ݣܻ ڄযઉ ח ޛ ह যڃ ࢝ӭੋо? • ز ۽Ӓې߁ (Dynamic Programming): Ѿҗܳ ೞݴ ಹח ޙઁ • द) ೖࠁա, ઁੌ ૣ ӡ ӝ • NP-Hard Problem: ??? • द) য ী ೧ࢲ Ӓ 0غח ࠗ࠙ ਸ ਸ ࣻ ਸө?
,JOEPG3FBTPOJOH *OUSPEVDUJPO3FBTPOJOH • ٜ ୶ܻ ޙઁܳ դب৬ ౠী ٮۄࢲ ֎
о۽ ࠙ܨೣ • ా҅ ਃড (Summary Statistics): য ੑ۱ਸ ‘ࣁח’ ޙઁ • द) ز ࢎী ݻ ѐ ա? Ҋনی ѐী যڃ Ѫ ݆ա? • ҙ҅ ࢲৌ (Relational Argmax): য ੑ۱ীࢲ ࢚ ҙ҅ܳ ٮח ޙઁ • द) о ݣܻ ڄযઉ ח ޛ ह যڃ ࢝ӭੋо? • ز ۽Ӓې߁ (Dynamic Programming): Ѿҗܳ ೞݴ ಹח ޙઁ • द) ೖࠁա, ઁੌ ૣ ӡ ӝ • NP-Hard Problem: ??? • द) য ী ೧ࢲ Ӓ 0غח ࠗ࠙ ਸ ਸ ࣻ ਸө?
,JOEPG3FBTPOJOH *OUSPEVDUJPO3FBTPOJOH • ٜ ୶ܻ ޙઁܳ դب৬ ౠী ٮۄࢲ ֎
о۽ ࠙ܨೣ • ా҅ ਃড (Summary Statistics): য ੑ۱ਸ ‘ࣁח’ ޙઁ • द) ز ࢎী ݻ ѐ ա? Ҋনی ѐী যڃ Ѫ ݆ա? • ҙ҅ ࢲৌ (Relational Argmax): য ੑ۱ীࢲ ࢚ ҙ҅ܳ ٮח ޙઁ • द) о ݣܻ ڄযઉ ח ޛ ह যڃ ࢝ӭੋо? • ز ۽Ӓې߁ (Dynamic Programming): Ѿҗܳ ೞݴ ಹח ޙઁ • द) ೖࠁա, ઁੌ ૣ ӡ ӝ • NP-Hard Problem: ࠺Ѿ ౚ݂ӝ҅о ೦दрী ಽ ࣻ ח ޙઁܳ ೧ ޙઁٜ۽ ೦ दрী ജਗೡ ࣻ ח • द) য ী ೧ࢲ Ӓ 0غח ࠗ࠙ ਸ ਸ ࣻ ਸө?
,JOEPG3FBTPOJOH *OUSPEVDUJPO3FBTPOJOH • ٜ п ޙઁٜী ೧ࢲ যڃ न҃ݎ ҳઑ۽
ಽ ࣻ ח ೞҊ ೞ • ా҅ ਃড (Summary Statistics): য ੑ۱ਸ ‘ࣁח’ ޙઁ • द) GNNҗ Deep Setਵ۽ח ੜ ಽ ࣻ ݅, MLP۽ח ಽӝ য۰ • ҙ҅ ࢲৌ (Relational Argmax): য ੑ۱ীࢲ ࢚ ҙ҅ܳ ٮח ޙઁ • द) GNNਵ۽ח ಽ ࣻ ݅, Deep Setਵ۽ח ಽӝ য۰ • ز ۽Ӓې߁ (Dynamic Programming): Ѿҗܳ ೞݴ ಹח ޙઁ • द) GNNਵ۽ ಽ ࣻ • NP-Hard Problem: ࠺Ѿ ౚ݂ӝ҅о ೦दрী ಽ ࣻ ח ޙઁܳ ೧ ޙઁٜ۽ ೦ दрী ജਗೡ ࣻ ח • द) Exhaustive Searchо ਃೣ
"MHPSJUIN"MJHONFOU
4USVDUVSF$PNQMFYJUZPG1SPCMFN "MHPSJUINJD"MJHONFOU • ޙઁ৬ Ӓ ޙઁܳ ಹח न҃ݎ ҳઑܳ о೧ࠁӝ
• ݅ডী ޙઁ ҳઑܳ न҃ݎ ҳઑী दఆ ࣻ ݶ?
4USVDUVSF$PNQMFYJUZPG1SPCMFN "MHPSJUINJD"MJHONFOU • ޙઁ ಽ ҙীࢲ ֤ޙ ਸ ઁೣ
4USVDUVSF$PNQMFYJUZPG1SPCMFN "MHPSJUINJD"MJHONFOU • ޙઁ ಽ ҙীࢲ ֤ޙ ਸ ઁೣ
4USVDUVSF$PNQMFYJUZPG1SPCMFN "MHPSJUINJD"MJHONFOU • ޙઁ ಽ ҙীࢲ ֤ޙਸ ਸ ઁೣ •
೧ࢳ: • ઑѤਸ ݅ೞח ޙઁח GNNਵ۽ दఆ ࣻ • Ӓ ޙઁী աఋաח ޛח ୭ Nѐө݅ оמೞҊ, • Ӓ ޙઁ ޛ ࣽࢲח ޙઁܳ ಹחؘ ޖҙೞ
4USVDUVSF$PNQMFYJUZPG1SPCMFN "MHPSJUINJD"MJHONFOU • ޙઁ ಽ ҙীࢲ ֤ޙਸ ਸ ઁೣ •
೧ࢳ: • ઑѤਸ ݅ೞח ޙઁח GNNਵ۽ दఆ ࣻ • ӒܻҊ GNN MLP۽ दఆ ࣻ
4USVDUVSF$PNQMFYJUZPG1SPCMFN "MHPSJUINJD"MJHONFOU • ޙઁ ಽ ҙীࢲ ֤ޙਸ ਸ ઁೣ •
೧ࢳ: • ઑѤਸ ݅ೞח ޙઁח GNNਵ۽ दఆ ࣻ • ӒܻҊ GNN MLP۽ दఆ ࣻ • ޙઁ: “ޙઁ ण” ҃ب Ӓۡө???
4USVDUVSF$PNQMFYJUZPG1SPCMFN "MHPSJUINJD"MJHONFOU • ޙઁ : ޙઁ णী ೧ࢲب Ӓۡө? •
೧ࠁݶ MLPח ੜ ޅ ߓח Ѫ э: ҳઑਵ۽ ޙઁܳ ୶࢚ചೞח מ۱ ࠗ೧ࢲ • GNN п ױ҅ܳ ߓ ࣻ ݅, MLPۄݶ ܖ Ѿҗޛਸ ೞա۽ ߓਕঠ ೣ
1"$-FBSOBCJMJUZBOE"MJHONFOU "MHPSJUINJD"MJHONFOU • Probably approximately correct learning (Ѣ ഛೠ ण)
• ੑ۱җ ۱हਸ ࢠ݂೧ࢲ णೞח ঌҊ્ܻਸ ࢤп೧ࠁݶ • Probably: ֫ ഛܫ۽ • Approximately Correct: ݽ؛ ী۞о Threshold ݅ੌѢ
1"$-FBSOBCJMJUZ "MHPSJUINJD"MJHONFOU • Probably approximately correct learning (Ѣ ഛೠ ण)
• ೧ࢳ: • ܻ ݽ؛ ࢠ݂ਸ ా೧ࢲ णदఆ ࣻ Ҋ о೧ࠁ (PAC ઑѤ) • Ӓ۞ݶ ী۞بܳ ઁೠೞӝ ਤ೧ࢲח ݆ ࡳইঠ ೠ (୭ࣗ M ݅ఀ ࠂب۽ ࡳইঠೣ)
1"$-FBSOBCJMJUZ "MHPSJUINJD"MJHONFOU • Probably approximately correct learning (Ѣ ഛೠ ण)
• ؊ ए ೧ࢳ: • ࢠ݂ਵ۽ णदఆ ٸ, ࢿמ ֫۰ݶ ݆ ࡳইঠೠ!
1"$-FBSOBCJMJUZ&YBNQMF "MHPSJUINJD"MJHONFOU • MLPח ݽٚ ࢎѾ җਸ ೞա ܖ۽ рೡ
ࣻ ߆ী হਵ۽
1"$-FBSOBCJMJUZ&YBNQMF "MHPSJUINJD"MJHONFOU • MLPח ݽٚ ࢎѾ җਸ ೞա ܖ۽ рೡ
ࣻ ߆ী হਵ۽ • ೧ࢳ: • MLP۽ ޙઁܳ ಽѱ दః۰ݶ • ୭ࣗೠ Layer ӝ ী ೧ ੑ۱ ରਗ ӝ ݅ఀ Ѣٟઁғೠ Ѫ • ӒѪਸ فߣ૩ Layer ӝী ೧ ғೠ Ѫ • ݅ఀ ࢠ݂೧ঠ णदఆ ࣻ ਸ Ѫ
1"$-FBSOBCJMJUZBOE"MJHONFOU "MHPSJUINJD"MJHONFOU • ݽ؛ PAC ള۲ оמ ઑѤਸ ݅ೠݶ
Alignment ܳ ࣻधਵ۽ ॶ ࣻ • ೧ࢳ: • ޙઁী ೧ࢲ Ӓ ޙઁܳ f1, f2, f3,..ਵ۽ ଂѓ ࣻ Ҋ оೞҊ • Ӓ ଂѐ ٜࠗ࠙ਸ пп न҃ݎਵ۽ दఆ ࣻ Ҋ оೞݶ • Alignment ػח Ѫ • пп ݽ؛ਸ ള۲दఆ ٸ, ӝઓী Mѐ ࡳ Ѫࠁ ѱ ࡳইب غח ҃
1"$-FBSOBCJMJUZBOE"MJHONFOU "MHPSJUINJD"MJHONFOU • ݽ؛ PAC ള۲ оמ ઑѤਸ ݅ೠݶ
Alignment ܳ ࣻधਵ۽ ॶ ࣻ • ؊ ए ೧ࢳ: • য۰ ޙઁܳ ए ࣁࠗ ޙઁ۽ ଂѐࢲ णदௌਸ ٸ, • Ӓ ए ࣁࠗ ޙઁ пп ؘఠ۽ب ੜ ߓ ࣻ ਵݶ જѷ!
#FUUFS"MJHONFOU#FUUFS(FOFSBMJ[BUJPO "MHPSJUINJD"MJHONFOU • Alignmentܳ ੜ ೡ ࣻ ח ݽ؛ ੜ
ߓ ࣻ • ઑѤਸ ݅ೠח о ೞীࢲ….
#FUUFS"MJHONFOU#FUUFS(FOFSBMJ[BUJPO "MHPSJUINJD"MJHONFOU • Alignmentܳ ੜ ೡ ࣻ ח ݽ؛ ੜ
ߓ ࣻ • ઑѤਸ ݅ೠח о ೞীࢲ….
#FUUFS"MJHONFOU#FUUFS(FOFSBMJ[BUJPO "MHPSJUINJD"MJHONFOU • Alignmentܳ ੜ ೡ ࣻ ח ݽ؛ ੜ
ߓ ࣻ • ппਸ ੜ ଂѐࢲ ߓח স ੜ ഛ݀ػݶ Ӓۧѱ ػח ܻࣗ • ցޖ োೠ ݈ੋ٠ • ܻ ҙীࢲ BERTо MLP ա LSTM ࠁ ੜೞח ਬח? • BERT ҳઑо ޙ ਫ਼ੋ ҳઑܳ ؊ Generalization ਸ ੜ ೮ӝ ٸޙ
#FUUFS"MJHONFOU#FUUFS(FOFSBMJ[BUJPO "MHPSJUINJD"MJHONFOU • Generalization ਸ ੜ ޅೞח ݽ؛ য۰ ޙઁܳ
णೞӝ য۰ • ࣘغѱ ݈ೞݶ ݽ؛ ࠂبী ٮۄ Ӓ ޙઁܳ ಽ “ल” ࠁੋח Ѫ • п ޙઁী ೠ ࢿמ
#FUUFS"MJHONFOU#FUUFS(FOFSBMJ[BUJPO "MHPSJUINJD"MJHONFOU • Generalization ਸ ੜ ޅೞח ݽ؛ য۰ ޙઁܳ
णೞӝ য۰ • ࣘغѱ ݈ೞݶ ݽ؛ ࠂبী ٮۄ Ӓ ޙઁܳ ಽ “ल” ࠁੋח Ѫ • Monster Trainer (Path Searching) ޙઁী ೠ п ݽ؛ ࢿמ
$PODMVTJPO
$PODMVTJPO $PODMVTJPO • যڃ न҃ݎ ݽ؛ਸ ഝਊ೧ࢲ যڃ ޙઁী ೧ࢲ
ಽ ٸ • Ӓ ݽ؛ ҳઑо ޙઁ ࢲࢎ ҳઑܳ ನҚೡ ࣻ যঠ ೣ • ݅ড Ӓۧ ঋݶ ই ݆ ࢠ ਃೣ = ݽ؛ ޙઁܳ ੌ߈ചೡ ࣻ হ • ࠂೠ ݽ؛ ԙ ੜ ಽח ঋ݅, рױೠ ݽ؛ ಽӝ য۰ • ܻীѱ ח दࢎ • औѱ ߓ ݽ؛, рױೠ ഋక۽ ҳࢿػ ݽ؛ য ޑೣਸ ੜ աఋյ ࣻ ਸө? • അ BERT ҳઑח ցޖ ࠂೠ Ѫ ইקө?