neural turing machines - meetupfiles.meetup.com/1406240/2016-06-23_ml-ny-meetup.pdf · conclusion...

Neural Turing MachinesTristan Deleu

@tristandeleu! June 23, 2016

Deep Learning

The building blocks

ConvolutionalLayer

Fully connectedLayer

RecurrentLayer

+

Object Recognition Object Detection Image Segmentation

Others

"

"

"

Predictions" Speech Recognition Language Processing

"

"

Examples

+ =

Object Detection

Predictions

+ =

PredictionsSpeech Recognition

+

Image Segmentation

Predictions

Face detection

Automatic speech recognition

=

Image segmentation

Examples

+ +

Object Recognition

Language Processing

Predictions

Sentiment analysis

Image captioning

Machine translation

=

=

=

+

Language Processing

+

Language Processing

Predictions

Language Processing

Frameworks

TheanoTorch

Tensorflow

Keras

Chainer

Neon

CNTK

MXNet

Caffe

LasagneLasagne

Theano + Lasagne

https://github.com/Lasagne/Lasagne/blob/master/examples/mnist.py

Neural Turing Machines

Recurrent Neural Network

ht

yt

xt

ht+1

yt+1

xt+1

yt�1

ht�1

xt�1

LSTMtLSTMt�1 LSTMt+1

Memory-augmented Networks

BOAT

Neural Network

Boats float on water You can’t sail against the wind Boats do not fly …

?

• Inspired by neuroscience

• Memory-augmented networks: add an external memory to neural networks to act as a knowledge base

• Keep track of intermediate computations — The story to answer the question in QA problems Memory Networks & Dynamic Memory Networks

Memory-augmented Networks

Memory Networks Dynamic Memory Networks Neural GPU

Neural Stack/Queue/DeQue Stack-augmented RNN

Current state Read Operation New state Write

0 0

1 0

0 1

1 0

Turing Machine

0 1 101010 1 0q0

q0

q0 q0

q0

q1

q1

q1

q1

· · ·

Neural Turing Machine

0 1 101010 1 0q0

Current state Read Operation New state Write

0 0

1 0

0 1

1 0

q0

q0 q0

q0

q1

q1

q1

q1

· · ·

Input Output

?

Heads

0 1 101010$

wt

Mt

$ $ $

Turing Machine Neural Turing Machine

Neural Turing Machine

FFt

ht

yt

rt

xt

xt

FFt+1

ht+1

yt+1

rt+1

xt+1

xt+1

yt�1

ht�1

rt�1

FFt�1

xt�1

xt�1

Mt�1 Mt

%&Controller

%'Read heads

%(Write heads

Neural Turing Machine

ht

yt

rt

xt

xt

ht+1

yt+1

rt+1

xt+1

xt+1

yt�1

ht�1

rt�1

xt�1

xt�1

Mt�1 Mt

LSTMtLSTMt�1 LSTMt+1

%&Controller

%'Read heads

%(Write heads

Neural Turing Machine

)

%*Memory

%&Controller

%'Read heads

%(Write heads

Input Output

&

NTM

Open-source Library

medium.com/snips-ai

github.com/snipsco/ntm-lasagne+

(

NTM-Lasagne

Algorithmic Tasks

• Goal: Learn full algorithms only from input/output examples Generate as much data as we need

• Strong Generalization: Generalize beyond the data the NTM has seen during trainingLonger sequences for example

,?Input Output

P (X,Y )

?

Copy taskInputs

Outputs

EOS

Training

Copy task

Copy task

Copy task

Length 120

Copy task

Length 150

Repeat Copy task

x5EOS

Inputs

Outputs

Repeat Copy task

Repeat Copy task

Associative Recall taskInputs

Outputs

Associative Recall task

Associative Recall task

Priority Sort task

bAbI tasks

bAbI tasks

Mary

John

bathroom

garden

Sandra

hallway

Mary

John

bathroom

garden

Sandra

hallway

Mary went to the garden John went to the garden Mary went back to the hallway Sandra journeyed to the bathroom John went to the hallway Mary went to the bathroom

bAbI tasks

Conclusion

• The NTM is able to learn algorithms only from examples

• It shows better generalization performances compared to other recurrent architecturesFor example LSTMs

• Fully differentiable structureDrawback: generalization is still not quite perfect

• New take on Artificial IntelligenceTrying to teach machines things they can do, the same way we would learn them

• Resources • Theano: http://deeplearning.net/software/theano/ • Lasagne: http://lasagne.readthedocs.io/en/latest/ • NTM-Lasagne: https://github.com/snipsco/ntm-lasagne

@tristandeleu! June 23, 2016

Thank you

@tristandeleu! June 23, 2016