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Preferred Networks
[email protected]
2017/3/3IPAB2017
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Preferred Networks (PFN)
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l NP NumberParameter SNPs
l
7/50
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2012
8
[Dahl+ 14]
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DNN[Goh+ 17]
DNN
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[Goh+ 17]
LR: RF: ST-NN: DNNMT-DNN: DNN
/PCBA, MUV, Tox21DNNDNN
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DNNl 3DNN
3
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one-shot
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Diet Network (1/3) [Romero+ 16]
l NP np n
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Diet Network (2/3)
l 1WeDNN(b) XTWeXT>> We
l X
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Diet Network (3/3)
l 1000GenomeDNASNPs 315345SNP, 5% 0.5
SNP2Vec1/1000
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18/50
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19/50
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20/50
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21/50
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VAE2
784
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P(x)
VAE
GAN
Q(x)/P(x)
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l
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[Gomez-Bombarelli+ 17]
l l z
SMILESzVAE
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DNN (1/2) [Altae-Tran+ 16]
l
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l
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DNN[Thompson+ 16]
l DNN DNN
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AlphaChem [Segler+ 17]
l retrosynthesis
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AlphaChem: Retrosynthesis
l 1
u NPu 20010~20100
1-10
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l AlphaGo
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AlphaChem:
l MCTS (Monte Carlo Tree Search RollOut
l vUCT P(a) a N(v) v Q(v) v c 3
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AlphaChem:
l 40 2, CPU BFS9000MCTS9000 BFS
l MCTS
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microRNAbindingDeep Target [Lee+ 16]
37/50
RNA, miRNA
RNN
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PFN
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0101011100011
DNN
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0101011100011
DNN
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AI2016/11/29with , AI
,Precision Medicine
41
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90%
99%
80%
microRNA[Shimomura+ Cancer Science 2016]
+
Deep Learning
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l l Grail
IlluminaGoogleX, IlluminaJeff HuberCEO
$900millionBLiquid Biopsy
l iPS++Single Cell+
l
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0101011100011
DNN
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45
/
1E100E Flops1 1TB101000, 100
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10P Flops1500010 [Baidu 2015]
100P 1E Flops10MSNPs100100PFlops11EFlops
10P) 10E Flops1 100006 [Google 2015]
/
1E100E Flops11TB1001
10PF 100EF100PF 1EF 10EF
P:Peta E:ExaF:Flops
1GB11TFlops
1
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PFN
l 2flops200GPU
10flops1000GPU 10 Flops, 2012
Baidu Minwa 0.6 Flops (2015GoogleGPU>100PF
l 20191 DL ops
11 DL ops, 1 DL ops
l GPU+HPC
46
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0101011100011
DNN
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Chainer as an open-source project
l https://github.com/pfnet/chainerl 101 contributorsl 2,128
stars & 564 forkl 7,335 commitsl Active development &
release
v1.0.0 (June 2015) to v1.20.1 (January 2017)
48
Original developerSeiya Tokui
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ChainerMN Imagenet204.4
ChainerMNdeveloperTakuya Akiba
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0101011100011
DNN
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VAT:[Miyato+ 16]l *
Takeru Miyato
* CIFAR-10, SVHN
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[Hu+ 17]
IMSAT: VAT
Hash
2016 PFN Intern
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l 110301arXiv
l
l GAN
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l
l
l
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[]l [Dahl+14] Multi-task Neural Networks for QSAR Predictions,
G. E.
Dalh, N. Jaitly, R. Salakhutdinov
l [Goh+ 17] Deep Learning for Computational Chemistry, G. B.
Goh, N. O. Hodas, A.Vishnu, arXiv:1701.04503
l [Romero+ 16] Diet Networks: Thin Parameters for Fat Genomics,
A. Romero, and et. al. arxiv:1611.09340
l [ 16] , IIBMP 2016l [Lin+ 16] Why does deep and cheap learning
work so well?, H. W.
Lin, M. Tegmark
l [Mao+ 16] Least Squares Generative Adversarial Networks, X.
Mao. And et. al. arxiv:1611.04076
l [Gomez-Bombarelli+ 17] Automatic Chemical Design using a
data-driven continous representation ofmolecules, R.
Gomez-Bombarelli, and et, al.arxiv:1610.02415
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l [Tomspson+ 16] Accelerating Eulerian Fluid Simulation with
Convolutional Networks, J. Tompson, and et. al.
arxiv:1607.03597
l [Altae-Tran+ 16] Low Data Drug Discovery with One-shot
Learning, H. Alatae-Tran, and et. al. arxiv:1611.03199
l [Segler+ 17] Towards AlphaChem: Chemical Synthesis Planning
with Tree Search and Deep Neural Network Policies, M. Segler and
et. al. arxiv:1702.00020
l [Lee+ 17] DeepTarget: End-to-end Learning Framework for
microRNA Target Prediction using Deep Recurrent Neural Networks, B.
Lee, and et.al, arxiv:1603.09123
l [Miyato+ 16] Distributional Smoothing with Virtual Adversarial
Training, T. Miyato, and et. al. ICLR 2016
l [Hu+ 17] Learning Discrete Representations via Information
Maximization Self Augmented Training, W. Hu and et al.
arxiv:1702.08720
