9. Graph Net
NP 速 (!?)
Graph Networks
Learning a SAT Solver from Single-Bit Supervision (Selsam+ ICLR2019)
Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision
TSP (Prates+ AAAI2019)
Learning Loop Invariants for Program Veri cation (Si+ NIPS2018)
Learning Combinatorial Optimization Algorithms over Graphs (Khalil+ NIPS2017)
Learning Graphical State Transitions (Johnson ICLR2017)
part position paper, part review, and part uni cation (DeepMind)
Battaglia+ 2018
Relational inductive biases, deep learning, and graph networks
https://arxiv.org/abs/1806.01261
https://github.com/deepmind/graph_nets
10. )
https://keras.io/examples/addition_rnn/
Input: "535+61" Output: "596"
{'5', '3', '5', '+', '6', '1'} {'5', '9', '6'}
bAbI (QA) RNN ( ) addition task
5 1 LSTM 99% heuristics( reversing)
n
/QSAR
CH3
N
N
H
N
H
H3C
N
0.739
H3C
H3C
NH
O
N
O
NO
1.399
CH3
O N
NH2
O
CH3
Br
-1.753 CH3
N
H3C
H
NS
N
O
CH3
N
OH
1.394
CH3
CH3N
N
N
CH3H3C
H2N NH2
-3.171
n
(size transferability)
27. Theory-driven vs Data-driven
David Hand
Data-driven Theory-driven
All models are wrong, but some are useful
(George Box)
Theory-driven models can be wrong
But data-driven models cannot be wrong
http://videolectures.net/kdd2018_hand_data_science/
28. Theory-driven vs Data-driven
David Hand
Data-driven Theory-driven
All models are wrong, but some are useful
(George Box)
Theory-driven models can be wrong
But data-driven models cannot be wrong
or right
http://videolectures.net/kdd2018_hand_data_science/
29. Theory-driven vs Data-driven
David Hand
Data-driven Theory-driven
All models are wrong, but some are useful
(George Box)
Theory-driven models can be wrong
But data-driven models cannot be wrong
or right
Data-driven are not trying to describe an underlying reality.
so they could be poor or useless, but not wrong
But are merely intended to be useful
http://videolectures.net/kdd2018_hand_data_science/
30. With enough data, the numbers
speak for themselves.
Chris Anderson (2008)
cf.
32. ( )
feedback
速
...
Thomas Edison
Genius is 1% inspiration and 99% perspiration.
There is no substitute for hard work.
I have not failed. I've just found 10,000 ways
that won't work.
: = +
( )
Empirical optimization or "Edisonian empiricism"
34. ( 速 !)
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<latexit sha1_base64="aabjZM6SNm3NlwPbE7sqkAWyXzk=">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</latexit>
( )
“ ”
J’aime la
musique I love music
CH3
N
H3C
H
NS
N
O
CH3
N
OH
1.394
...
35. ( 速 !)
<latexit sha1_base64="DuksIrWdNAsvY6hvC/3omgtTDqo=">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</latexit>
<latexit sha1_base64="aabjZM6SNm3NlwPbE7sqkAWyXzk=">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</latexit>
( )
“ ”
J’aime la
musique I love music
CH3
N
H3C
H
NS
N
O
CH3
N
OH
1.394
...