自然科学における機械学習と機械発見

[email protected]
⾒
(ICReDD)
2022 11 1

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1995 2004 ( )
2004 ( ) " "
2005 2011 ( / )
2012 2018 ( )
JST ( )
2019 (ICReDD)
2019 ⾒ (AIP)
https://itakigawa.github.io iPS @ ATR
(iPS )

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1995 2004 ( )
2004 ( ) " "
2005 2011 ( / )
2012 2018 ( )
JST ( )
2019 (ICReDD)
2019 ⾒ (AIP)
iPS @ ATR
(iPS )
https://itakigawa.github.io

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Q.
ダ
ダ⾒
ダ Underspeci cation
ダ
May the ML force be with you

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(g) (cm)

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(g) (cm)
5 6.25 7.5 8.75 10
90 112.5 135 157.5 180
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(g) (cm)
5 6.25 7.5 8.75 10
90 112.5 135 157.5 180
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(g) (cm)
5 6.25 7.5 8.75 10
90 112.5 135 157.5 180
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(g) (cm)
5 6.25 7.5 8.75 10
90 112.5 135 157.5 180
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(g) (cm)
5 6.25 7.5 8.75 10
90 112.5 135 157.5 180
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(g) (cm)
5 6.25 7.5 8.75 10
90 112.5 135 157.5 180
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(g) (cm)
5 6.25 7.5 8.75 10
90 112.5 135 157.5 180
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(g) (cm)
5 6.25 7.5 8.75 10
90 112.5 135 157.5 180
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(g)
(cm)
む
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View Slide

(g) (cm)
5 6.25 7.5 8.75 10
90 112.5 135 157.5 180
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(g)
(cm)
む
む
(cm)
(g)
む
む

View Slide

む
む
or
5 6.25 7.5 8.75 10
90 112.5 135 157.5 180
●
●
●
●
●
●
●
●
●
●
(g)
(cm)
(cm)
(g)
む
む

View Slide

( )
J aime la
musique
I love music
⾒

View Slide

( )

View Slide

: (DALL
·E , Stable Diffusion, Midjourney, )
( + )

View Slide

( )
Decision
Tree
Random
Forest
GBDT
Nearest
Neighbor
Logistic
Regression
SVM
Gaussian
Process
Neural
Network

View Slide

View Slide

0 1
P( =red)

View Slide

Random Forest
Gaussian Process
Logistic Regression
0 1
P( =red)

View Slide

Random Forest Neural Network SVR Kernel Ridge
p1 p2 p3 p4

View Slide

=
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x2
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f
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x1 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
x2
N = = N = = N = =

View Slide

=
x1
x2
y
f
f
y
x2
( )
N = = N = = N = =

View Slide

N = = N = = N = =
=
x1
x2
y
f
f
y
x2
( )

View Slide

N = = N = = N = =
=
x1
x2
y
f
f
y
x2
( )
( )
( )

View Slide

テ Training ( ) デ Validation ( ) ト Test ( )
ダ
ダ (data leakage)

View Slide

SF (AI)
wishful mnemonics
Why Is AI So Dumb?
A SPECIAL REPORT
https://spectrum.ieee.org/ les/ / _Spectrum_ .pdf

View Slide

q1 q2 q3 q4
( )
Random
Forest
GBDT
Nearest
Neighbor
SVM
Gaussian
Process
Neural
Network
deep learning

View Slide

q1 q2 q3 q4
p1 p2 p3 p4
( )
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ResNet : 4500
E cientNet-B : 6600
VGG : 1 4400
⾒
20×20 1200
1000×1000 300
-layer, -heads BERT: 1 1000
-layer, -heads BERT: 3 3600
GPT- XL: 15 5800
GPT- : 1750
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Annu. Rev. Phys. Chem. : ( )
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⾒

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https://uclnlp.github.io/nampi/
Neural Abstract Machines & Program Induction
Di erentiable Neural Computers /
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Memory Networks (Weston+ )
Pointer Networks (Vinyals+ )
Neural Stacks (Grefenstette+ , Joulin+ )
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Neural Program Interpreters (Reed+ )
Neural Programmer (Neelakantan+ )
DeepCoder (Balog+ )
: ⾒

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my_function
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J aime la
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( )
バ
(2016 )

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X Y
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( ) ( )
( )
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Simon, H. Machine discovery. Found Sci 1, 171–200 (1995).
Herbert A. Simon
(1975)
(1978)
Human and machine discovery are gradual problem-solving processes of
searching large problem spaces for incompletely defined goal objects.
Research on problem solving has usually focused on search of an “instance
space” (empirical exploration) and a “hypothesis space” (generation of
theories). In scientific discovery, search must often extend to other spaces as
well: spaces of possible problems, of new or improved scientific instruments,
of new problem representations, of new concepts, and others. This paper
focuses especially on the processes for finding new problem representations
and new concepts, which are relatively new domains for research on discovery.
( )
/

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自然科学における機械学習と機械発見

自然科学における機械学習と機械発見

itakigawa

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