ROB 537: Learning Based Control
Week 6, Lecture 2 Recurrent Neural Networks
Artificial Neural Networks
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Your Name | Event Name 1 Assumptions
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Your Name | Event Name 2
Example: XOR
Hidden layer •
– Output layer Input 1
Input 2
Your Name | Event Name 3 Example: XOR with single input channel
Hidden layer •
– Output layer
• Input
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Your Name | Event Name 4
Example: XOR with single input channel; Hidden
• Intermediate output
– Input 1 t1
Hidden
• Output
– Input 2
t2 –
Your Name | Event Name 5 Example: XOR
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Your Name | Event Name 6
Example: XOR
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Your Name | Event Name 7 Example: XOR
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Your Name | Event Name 8
Some approaches
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9 Some approaches
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Some approaches
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• Recurrent Neural Networks (RNNs)
– Use ‘recurrent connections’ to bridge information flow across time
11 XOR problem with one input channel Hidden
Intermediate output
Input 1 t1
Hidden
Output
Input 2
t2
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XOR problem with one input channel Hidden
• Intermediate output
Input 1 • t1
Hidden
Output
Input 2
t2
Recurrent Connection
13 XOR problem with one input channel Hidden
• Intermediate output
Input 1 • t1
Hidden
Output
Input 2
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Recurrent Connection
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Recurrent Connections Hidden
• Intermediate output
Input 1 t1
Hidden
Output
Input 2
t2
15 Recurrent Connections Hidden
• Intermediate output
Input 1 t1
Hidden
Output
Input 2
t2
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Recurrent Connections Hidden
• Intermediate output
Input 1 t1
Hidden
Output
Input 2
t2
17 Recurrent Neural Networks
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.
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Recurrent Neural Networks
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.
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19 Recurrent Neural Networks
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.
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Overview of RNNs
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21 Overview of RNNs
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Training a RNN
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23 Solution
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Solution
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Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.
25 Gating mechanisms
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Gating mechanisms
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Input Hidden
Output
Input gate
27 Standard RNN (SRN)
Source: Deeplearning4J
Source: Deeplearning4J
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Long short term memory (LSTM)
Source: Deeplearning4J
29 Long short term memory (LSTM)
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LSTM (Chung et al.)
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Gated Recurrent Unit (GRU)
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GRU (Chung et al.)
31 GRU and LSTM
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GRU (Chung et al.) LSTM (Chung et al.)
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Topology Limitation
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GRU (Chung et al.) LSTM (Chung et al.)
33 Gated Recurrent Unit with Memory Block (GRU-MB)
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Khadka, Chung and Tumer. GECCO 2017
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Feedforward NN
35 Read from external memory
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Write to memory
37 Gate Input
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Gate what’s read from memory
39 Gate what’s written to memory
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Topology comparison
GRU-MB
GRU (Chung et al.) LSTM (Chung et al.)
41 Topology comparison
GRU-MB
GRU (Chung et al.) LSTM (Chung et al.)
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Topology comparison
GRU-MB
GRU (Chung et al.) LSTM (Chung et al.)
43 RNNs with Attention Mechanisms
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NTM (Graves 2016)
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