Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
What Can Neural Networks Reason About?
Search
Scatter Lab Inc.
May 29, 2020
Research
0
2.2k
What Can Neural Networks Reason About?
Scatter Lab Inc.
May 29, 2020
Tweet
Share
More Decks by Scatter Lab Inc.
See All by Scatter Lab Inc.
zeta introduction
scatterlab
0
1.8k
SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
scatterlab
0
4.1k
Adversarial Filters of Dataset Biases
scatterlab
0
2.2k
Sparse, Dense, and Attentional Representations for Text Retrieval
scatterlab
0
2.3k
Weight Poisoning Attacks on Pre-trained Models
scatterlab
0
2.2k
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
scatterlab
0
2.5k
Beyond Accuracy: Behavioral Testing of NLP Models with CheckList
scatterlab
0
2.3k
Open-Retrieval Conversational Question Answering
scatterlab
0
2.3k
Exploring the Limits of Transfer Learning with Unified Text-to-Text Transformer
scatterlab
0
2.2k
Other Decks in Research
See All in Research
Stealing LUKS Keys via TPM and UUID Spoofing in 10 Minutes - BSides 2025
anykeyshik
0
120
Combinatorial Search with Generators
kei18
0
780
Agentic AIとMCPを利用したサービス作成入門
mickey_kubo
0
550
PhD Defense 2025: Visual Understanding of Human Hands in Interactions
tkhkaeio
1
190
snlp2025_prevent_llm_spikes
takase
0
170
[論文紹介] Intuitive Fine-Tuning
ryou0634
0
110
ストレス計測方法の確立に向けたマルチモーダルデータの活用
yurikomium
0
1.5k
国際論文を出そう!ICRA / IROS / RA-L への論文投稿の心構えとノウハウ / RSJ2025 Luncheon Seminar
koide3
6
4.7k
SNLP2025:Can Language Models Reason about Individualistic Human Values and Preferences?
yukizenimoto
0
130
EOGS: Gaussian Splatting for Efficient Satellite Image Photogrammetry
satai
4
520
SSII2025 [TS1] 光学・物理原理に基づく深層画像生成
ssii
PRO
4
4.2k
EcoWikiRS: Learning Ecological Representation of Satellite Images from Weak Supervision with Species Observation and Wikipedia
satai
3
140
Featured
See All Featured
Code Review Best Practice
trishagee
71
19k
ピンチをチャンスに:未来をつくるプロダクトロードマップ #pmconf2020
aki_iinuma
127
53k
Connecting the Dots Between Site Speed, User Experience & Your Business [WebExpo 2025]
tammyeverts
8
530
What's in a price? How to price your products and services
michaelherold
246
12k
The Psychology of Web Performance [Beyond Tellerrand 2023]
tammyeverts
49
3k
Helping Users Find Their Own Way: Creating Modern Search Experiences
danielanewman
29
2.9k
[Rails World 2023 - Day 1 Closing Keynote] - The Magic of Rails
eileencodes
36
2.5k
The MySQL Ecosystem @ GitHub 2015
samlambert
251
13k
Templates, Plugins, & Blocks: Oh My! Creating the theme that thinks of everything
marktimemedia
31
2.5k
Six Lessons from altMBA
skipperchong
28
4k
Why Our Code Smells
bkeepers
PRO
339
57k
Designing for Performance
lara
610
69k
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?