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
[読み会] Learning Representations by Humans, for H...
Search
mei28
April 19, 2022
0
27
[読み会] Learning Representations by Humans, for Humans
読み会資料
Learning Representations by Humans, for Humans(ICML2021)
mei28
April 19, 2022
Tweet
Share
More Decks by mei28
See All by mei28
[読み会] “Are You Really Sure?” Understanding the Effects of Human Self-Confidence Calibration in AI-Assisted Decision Making
mei28
0
83
[JSAI'24] 人間の判断根拠は文脈によって異なるのか?〜信頼されるXAIに向けた人間の判断根拠理解〜
mei28
1
490
[CHI'24] Fair Machine Guidance to Enhance Fair Decision Making in Biased People
mei28
0
55
[DEIM2024] 卓球の得点予測における重要要素の分析
mei28
0
37
[Human-AI Decision Making勉強会] 意思決定 with AIは個人vsグループで変わるの?
mei28
0
200
[読み会] Words are All You Need? Language as an Approximation for Human Similality Judgements
mei28
0
36
[参加報告] AAAI'23
mei28
0
89
[計算機構論] Learning Models of Individual Behavior in Chess
mei28
0
70
[計算機構論] Why do tree-based models still outperform deep learning on tabular data?
mei28
0
55
Featured
See All Featured
Fantastic passwords and where to find them - at NoRuKo
philnash
51
3k
How STYLIGHT went responsive
nonsquared
98
5.4k
GitHub's CSS Performance
jonrohan
1030
460k
We Have a Design System, Now What?
morganepeng
51
7.4k
Principles of Awesome APIs and How to Build Them.
keavy
126
17k
Creating an realtime collaboration tool: Agile Flush - .NET Oxford
marcduiker
27
1.9k
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
280
13k
Save Time (by Creating Custom Rails Generators)
garrettdimon
PRO
29
1k
XXLCSS - How to scale CSS and keep your sanity
sugarenia
248
1.3M
Designing Dashboards & Data Visualisations in Web Apps
destraynor
231
53k
個人開発の失敗を避けるイケてる考え方 / tips for indie hackers
panda_program
100
18k
Into the Great Unknown - MozCon
thekraken
35
1.6k
Transcript
ൃදऀɿ༶໌ ݄!ಡΈձ -FBSOJOH3FQSFTFOUBUJPOT CZ)VNBOT GPS)VNBOT
จใͱબΜͩཧ༝ બཧ༝ • ਓؒʹରͯ͠ใٕज़Ͱ࡞༻ʢհೖʣʹ͍ͭͯΔͨΊ • )VNBOJOUIFMPPQܥͷจ
ߩݙɿਓؒΛհͯ͠ਓؒʹ༗ӹͳදݱΛ֫ಘՄೳʹ എܠɿػցֶश͕ൃల͖ͯͯ͠ɼҙࢥܾఆʹΘΕ͖͍ͯΔ ɿ҆શੑɼެฏੑͳͲΛߟྀ͢ΔࡍʹӏವΈʹͣ͠Β͍ ఏҊɿਓ͕ؒཧղՄೳͳํ๏ͰใΛఏࣔ͠ɼ ਓؒͷ࠷ऴܾఆΛࢧԉ͢ΔϑϨʔϜϫʔΫ.P.ΛఏҊ ݁ՌɿͭͷλεΫΛ࣮ࢪɽ ਓؒͷҙࢥܾఆʹର্ͯͤ͠͞Δ͜ͱΛࣔͨ͠
͍··Ͱͷҙࢥܾఆࢧԉɿग़ྗΛӏವΈ͠ͳ͍ ػցͷग़ྗ Λਓ͕ؒड͚औΓɼߦಈ ΛͱΔ ̂ y a fθ ̂
y x a
͍··Ͱͷख๏ɿΑ͍ग़ྗΛֶशʢ܇࿅࣌ʣ ڭࢣ͋ΓֶशͰྑ͍ग़ྗʹͳΔΑ͏ʹֶश fθ ̂ y x y L(y, ̂
y)
ݱ࣮ɿਪ݁ՌͱߦಈҰக͠ͳ͍ ਓ͕ؒؒʹೖΔͷͰ ʹͳΔ͜ͱ͕͋Δ ҙࢥܾఆͷঢ়گʹΑͬͯػցͱਓؒͷҙࢥܾఆ͕Ұக͠ͳ͍ ̂ y ≠ a fθ
̂ y x a ≠ ̂ y
ཧɿਓؒͷҙࢥܾఆΛ࠷దԽ ਓؒͷߦಈΛؚΊͯ࠷దԽ͢Δ͜ͱ͕ཧ ͕ͨͩͷग़ྗͰਓؒʹͱͬͯཧղ͍͠ ̂ y y fθ ̂ y
x a = h(x, ̂ y) L(y, a)
ՄࢹԽ ఏҊख๏ɿਓؒͰཧղͰ͖Δํ๏Ͱࢧԉ͍ͨ͠ ਓ͕͍ؒΔͨΊɼޯΛͰ͖ͳ͍ y දݱֶश x ϕ a =
h(z, ̂ y) L(y, a) z
ਓؒͷཧϞσϧΛઃఆͯ͠ɼޯΛՄೳʹʂ y x ϕ a = h(z, x) L(y,
a) h z
දݱΛͬͨదͳՄࢹԽ͍͠ දݱ ͔Βਓ͕ؒཧղͰ͖ΔΑ͏ʹ͢ΔͨΊʹɼ ՄࢹԽͷૢ࡞͕ඞཁ • άϥϑɼը૾ɼϋΠϥΠτͱ͔ʜ ҙࢥܾఆʹରͯ͠ɼྑ͍հೖ͕ߦ͑ΔΑ͏ͳՄࢹԽͦΕ͚ͩͰݚڀʹ ͳΔ͘Β͍͍͠
ຊݚڀͰɼྑͦ͞͏ͳՄࢹԽΛબΜͰར༻͢Δ z
࣮ݧɿͭͷλεΫͰ༗ޮੑΛࣔ͢ λεΫɿߴ࣍ݩσʔλΛ࣍ݩʹѹॖͨ͠ͱ͖ʹ ༗༻ͳදݱΛ֫ಘͰ͖Δ͔ʁ λεΫɿ࣮ࡍͷλεΫͰ ɹɹɹɹ֫ಘͨ͠දݱʹΑΔࢧԉ༗ޮ͔ʁ λεΫɿػց͕Γಘͳ͍Ճใ
ɹɹɹɹදݱͱͯ֫͠ಘͰ͖Δ͔ʁ
λεΫɿྑ͍දݱΛ֫ಘͰ͖Δ͔ʁ ࣍ݩѹॖΛͯ͠σʔλͷՄࢹԽ͕ՄೳʹͳΔ • طଘͷ࣍ݩѹॖ౷ܭతͳ࠷దԽ͔͠ߟ͍͑ͯͳ͍ ߴ࣍ݩͳσʔλΛਓతʹ࡞ΓɼྨʹऔΓΉ • ަࣹӨͨ࣌͠ʹɼz9zͱz0zͷܗʹฒͿΑ͏ʹ࡞
λεΫɿදݱϞσϧͱཧϞσϧ දݱϞσϧ • Yͷઢܕࣸ૾ߦྻ ཧϞσϧ • ҰͰYͷΈࠐΈωοτϫʔΫ • ਓؒͷࢹ֮Λ͓͓·͔ʹ࠶ݱ͢Δ
λεΫɿ݁Ռ "DDVSBDZˠ ͷ࣮ݧࢀՃऀ͕ਫ਼Λୡ
λεΫɿϦΞϧͳσʔλͰࢧԉͰ͖Δ͔ ϩʔϯ৹ࠪΛࡐʹͨ͠λεΫ • ࡁͨ͠ɼ ೲɼ ঝೝɼ ڋ൱ • ଛࣦؔɿ
.5VSLͰࢀՃऀΛूΊܭճΛूΊͨ ඪɿදݱʹΑΔΞυόΠεͰ ҙࢥܾఆͷࢧԉ͕Մೳ͔Ͳ͏͔ y = 1 y = 0 a = 1 a = 0 l(y, a) = 1y≠a
λεΫɿإදʹΑͬͯදݱΛՄࢹԽ 'BDJBMBWBUBSΛͬͯɼإදͰՄࢹԽΛߦ͏ • $IFSOP ff GBDFTإύʔπʹͦΕͧΕ͕ରԠͯ͠มԽ
λεΫɿදݱϞσϧͱཧϞσϧ දݱϞσϧ • શ݁߹ͷϢχοτΛ ཧϞσϧ • શ݁߹ͷϢχοτΛ
λεΫɿతͳΞυόΠεͱಉ /PBEWJDFΑΓ্ ༧ଌΞυόΠεͱಉ
λεΫɿਓ͔ؒ͠Γಘͳ͍ใ֫ಘͰ͖Δ͔ ਓ͔ؒ͠Γಘͳ͍ใʢࣝɼৗࣝͱ͔ʣΛදݱͱͯ֫͠ಘ Ͱ͖Δ͔ΛΈ͍ͨ ҩྍஅͷλεΫΛઃܭ • Ճใ Λઃఆ͢Δˠਓ͔ؒ͠ݟΕͳ͍ • ͜ͷՃใग़ྗʹӨڹΛٴ΅͢ઃఆ
σʔληοτࣗମਓతʹੜ s
λεΫɿදݱϞσϧͱཧϞσϧ දݱϞσϧ • ॏΈͱಛྔͷઢܗ ཧϞσϧ • ॏΈͱಛྔͷઢܗ Ճใͷઢܗ
λεΫɿ • ७ਮʹ܇࿅Λߦ͏߹ .P. • දݱҰॹʹ܇࿅͢Δ .P.Ҏ֎ͰɼՃใʹ Αͬͯաֶश͕ى͖͍͢
h(.BDIJOF)
·ͱΊͱײ ·ͱΊ ਓ͔ؒΒਓؒͷͨΊʹҙࢥܾఆΛࢧԉ͢Δ ϑϨʔϜϫʔΫΛఏҊ ਓؒͷ࠷ऴܾఆΛࢧԉͰ͖ΔΑ͏ʹɼදݱΛֶश͠ՄࢹԽ