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Sequential TasksModel Size (≈)Models
LSTMGRURNNTCN (ours)
Seq. MNIST (accuracy)70K87.296.221.599.0
P-Seq. MNIST (accuracy)70K85.787.325.397.2
The Adding Problem T=600 (loss)70K0.1645.3e-50.1775.8e-5
Copy Memory T=1000 (loss)16K0.02040.019713.5e-5
Music JSB Chorales (loss)300K8.458.438.918.10
Music Nottingham (loss)1M3.293.46-3.07
Word-level PTB (ppl)13M84.7792.48114.5090.17
Word-level Wiki-103 (ppl)-48.4 (large)-145.19
Word-level LAMBADA (ppl)-4186-147251279
Char-level PTB (bpc)3M1.411.421.521.35
Char-level text8 (bpc)5M1.521.561.691.45
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TCN SETTINGS
Dataset/TaskSubtaskk nHiddenDropoutGrad ClipNote
The Adding ProblemT=20067 27
T= 40077 270.0N/A
T= 60088 24
Seq. MNIST78250.0N/A
68 20
Permuted MNIST7 68 825 0.0 20N/A
Copy Memory TaskT=5006910
T= 10008810 0.051.0RMSprop 5e-4
T= 200089 10
Music JSB Chorales1321500.5 0.4
Music Nottingham1641500.2 0.4
Word-level LMPTB34 600Embed. size 600
Wiki-10335 10000.40.3Embed. size 400
LAMBADA45 500Embed. size 500
Char-level LMPTB33450
text8250.1 5200.15Embed. size 100
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LSTM SETTINGS(KEY PARAMETERS)
Dataset/TaskSubtasknHiddenDropoutGrad ClipBiasNote
The Adding ProblemT= 200277505.0SGD 1e-3
T= 4002770.05010.0Adam 2e-3
T= 600113051.01
Seq. MNIST:11300.011.0RMSprop 1e-3
Permuted MNIST111300.0110.0RMSprop 1e-3
Copy Memory TaskT= 5001500.25
T=10001500.0511RMSprop/Adam
T= 20003281
Music JSB Chorales122000.2110.0SGD/Adam
Music Nottingham-32800.10.5-Adam 4e-3
Word-level LM15001-
PTB37000.40.31.0SGD 30,Emb. 700, etc.
Wiki-103--···Grave et al. (2016)
LAMBADA---1-Grave et al. (2016)
Char-level LMPTB6000.10.5-Emb. size 120
text82110240.150.51Adam 1e-2
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TCN VS. SoTA RESULTS
TaskTCN ResultSizeSoTASizeModel
Seq. MNIST (acc.)99.021K99.021KDilated GRU (Chang et al., 2017)
P-MNIST (acc.)97.242K95.942K Zoneout (Krueger et al., 2017)
Adding Prob. 600 (loss)5.8e-570K5.3e-570KRegularized GRU
Copy Memory 1000 (loss)3.5e-570K0.01170KEURNN (Jing et al., 2017)
JSB Chorales (loss)8.10300K3.47：DBN+LSTM(Vohra et al., 2015)
Nottingham (loss)3.071M1.32-DBN+LSTM (Vohra et al., 2015)
Word PTB (ppl)90.1713M52.824MAWD-LSTM + Cont.Cache(Merity et al., 2017)
Word Wiki-103 (ppl)45.19148M40.4>300MNeural Cache Model (Large)(Grave et al., 2016)
Word LAMBADA (ppl)127956M138>100MNeural Cache Model (Large)(Grave et al., 2016)
Char PTB (bpc)1.353M1.2214M2-LayerNorm HyperLSTM(Ha et al., 2017)
Char text8 (bpc)1.454.6M1.29>12MHM-LSTM (Chung et al., 2016)
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RELU TCN VS.GATED TCN RESULTS
TaskTCNTCN + Gating
Seq. MNIST (acc.)99.099.0
P-MNIST (acc.)97.296.9
Adding Prob. 600 (loss)5.8e-55.6e-5
CopyMemory 11000 (loss)3.5e-50.00508
JSB Chorales (loss)8.108.13
Nottingham (loss)3.073.12
Word PTB (ppl)90.1788.91
Char PTB (bpc)1.351.343
Char text8 (bpc)1.451.48
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