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Recurrent Neural
Network and its
Applications
in a nutshell
Sungjoon Choi

Kakao Brain
2
Part 1: Basics of RNNs
Recurrent Neural Network
3
Basic structure
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Recurrent Neural Network
4
Unfolding in time
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Recurrent Neural Network
5
Long term dependency
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Recurrent Neural Network
6
LongER term dependency
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Long Short Term Memory
7
Vanilla RNN
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Long Short Term Memory
8
Long Short Term Memory (LSTM)
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Long Short Term Memory
9
Overall structure
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Input
Output
Cell state Next cell state
Hidden state Next hidden state
Forget gate
Input
gate
Output
gate
Long Short Term Memory
10
Core idea
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
vs.
Long Short Term Memory
11
Core idea
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Long Short Term Memory
12
Step-by-step
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Forget gate
Input gate
Decide which information to throw away
from the cell state.
Decide which information to store to the
cell state.
Long Short Term Memory
13
Step-by-step
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Update
Output gate
Update the cell state scaled by input and
forget gates.
Output based on the updated cell state.
Long Short Term Memory
14
Reminder
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Input
Output
Cell state Next cell state
Hidden state Next hidden state
Forget gate
Input
gate
Output
gate
Gated Recurrent Unit
15
Variation of a LSTM
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Training a RNN
16
How do we train a RNN?
Training a RNN
17
How do we train a RNN?
Training a RNN
18
How do we train a RNN?
h1 = (WT
h0 + UT
x1)
h2 = (WT
(WT
h0 + UT
x1) + UT
x2)
h3 = (WT
(WT
(WT
h0 + UT
x1) + UT
x2) + UT
x3)
h3 = (WT
(WT
(WT
(WT
h0 + UT
x1) + UT
x2) + UT
x3) + UT
x4)
Training a RNN
19
How do we train a RNN?
h1 = (WT
h0 + UT
x1)
h2 = (WT
(WT
h0 + UT
x1) + UT
x2)
h3 = (WT
(WT
(WT
h0 + UT
x1) + UT
x2) + UT
x3)
h3 = (WT
(WT
(WT
(WT
h0 + UT
x1) + UT
x2) + UT
x3) + UT
x4)
Vanishing / exploding gradients often happen.
20
Part 2: RNN Applications
Papers
21
22
Cited over 1,000 times!
Prediction and Generation
23
How do we generate a sequence?
Prediction and Generation
24
How do we generate a sequence?
P(x) =
Y
t
P(xt|x1:t 1)
The Role of Memory
25
Need to remember the past to predict the future.
The Role of Memory
26
Need to remember the past to predict the future.
Basic Architecture
27
Prediction Network
28
The prediction network is optimized by minimizing the following loss:
TX
t=1
log P(xt|x1:t 1)
However, synthesis is NOT possible yet. Why?
Text Prediction
29
From Wikipedia
Text Prediction
30
From 구운몽
Handwriting Prediction
31
Mixture density network
The outputs of a neural network parametrize a mixture distribution.
The inputs consist of pen offsets and end of stroke.
Handwriting Prediction
32
Mixture density network
The input and output of a network
More specifically,
Handwriting Prediction
33
Mixture density network
Handwriting Synthesis
34
What is different?
Soft window
Characters
Handwriting Synthesis
35
Soft window handling
! ", $ %& "
'(
)
*(
+(
$
Biased Sampling
36
37
Image Captioning
38
Overall architecture
Machine Translation Model
39
Language Model
40
Language Model
41
Language Model
42
<Start> cat
h0
y0
Language Model
43
<Start> cat sat
h0
y0
h1
y1
Language Model
44
<Start> cat sat on
h0
y0
h1
y1
h2
y2
Language Model
45
<Start> cat sat on mat
h0
y0
h1
y1
h2
y2
h3
y3
Training Phase
46
Training Phase
47
Training Phase
48
Training Phase
49
Training Phase
50
Test Phase
51
Test Phase
52
Test Phase
53
Test Phase
54
Test Phase
55
Test Phase
56
Test Phase
57
Test Phase
58
Results
59
Results
60
61
Show, Attend, and Tell
62
Soft Attention Mechanism
63
CNN RNN
Soft Attention Mechanism
64
Attention
Soft Attention Mechanism
65
Results
66
Results
67
Results
68
Results
69
70
DenseCap
71
Model Architecture
72
Object detection Image captioning
Soft spatial attention
Results
73
Results
74
75
Deep Tracking
76
The goal is to reveal the occluded parts of a scene by learning to track objects
from raw sensor data.
Filtering with RNNs
77
Filtering with RNNs
78
Results
79
80
Proposed Architecture
81
Binary	
Occupancy	
Grids
First	Layer	Output Second	Layer	Output
Proposed Architecture
82
Recurrent Flow Network
Update Rule
83
Update Rule
84
Context	Propagation
Update Rule
85
Next	Input
Update Rule
86
Correction
Context	Propagation
Update Rule
87
Next	Input
Propagation
Prediction
Update Rule
88
Results
89
Noise (5%)
Results
90
Noise (10%)
Results
91
Noise (20%)
Results
92
Noise (30%)
Results
93
Ford LiDar dataset
94
Social LSTM
95
This paper proposed an LSTM model which can learn general human movement
and predict their future trajectories.
A new model called SocialLSTM which can jointly predict the paths of all the
people in a scene by taking into account the common sense rules and social
conventions is presented.
SocialLSTM
96
SocialLSTM
97
A new social pooling method is presented by incorporating a social hidden
tensor per each LSTM.
Social pooling
SocialLSTM
98
Pose estimation
A new social pooling method is presented by incorporating a social hidden
tensor per each LSTM.
Future position of a pedestrian is modeled via a bivariate Gaussian distribution:
Results
99
100
DESIRE
101
This paper introduced a Deep Stochastic IOC RNN Encoder decoder framework,
DESIRE, for the task of future predictions of multiple interacting agents in
dynamic scenes.
Following mechanisms are focussed:

Diverse Sample Generation

IOC-based Ranking and Refinement

Scene Context Fusion
Proposed Architecture
102
Proposed Architecture
103
Sample Generation Module
Both previous and future trajectories are encoded with RNN Encoder1 and RNN
Encoder2.
Conditional VAE samples a latent vector and fed into RNN Decoder1 to generate
a future trajectory where the reconstruction error is minimized.
Results
104
105
Predictive State Recurrent Neural Networks
106
Many of time series modeling methods can be categorized as either recursive
Bayes Filtering or Recurrent Neural Networks. 

Recursive Bayes Filtering

Hidden Markov models (HMMs) or Kalman fitering (KF)

Predictive State Representation (PSR) is a variation on Bayes filters that
represents a state as the statistics of a distribution of features of future
observations. 

Recurrent Neural Network (RNN)

RNNs model sequential data via a parameterized internal state and
update function.
Predictive State Recurrent Neural Networks
107
Proposed architecture
Results
108
Swimmer

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