learned representations

Learned Representations

@ejlbell

Me

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Lyst

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Feature engineering

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Type of transform Examples

Unary exp, log, sqrt

Normalisation mean, variance

Aggregation count, sum, mean

Dimensional reduction PCA, clustering, manifold

Text tagging, parsing, stemming

Image histograms, key points, super pixels, segmentation

Others temporal / spatial

AI must fundamentally understand the

world around us and this can only be

achieved if it can learn to identify and

disentangle the underlying explanatory

factors hidden in the observed milieu of

low-level sensory data.

2014 - Representation Learning: A Review and New Perspectives. - Bengio et al.

Human ingenuity and prior knowledge

Feature Engineering

Representation Learning

Sufficiently powerful models that learn “good” feature transforms

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Deep Learning

Image Filters

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VGG

Representations

2015 - Very Deep Convolutional Networks for

Large-Scale Image Recognition. Simonyan and

Zisserman

Regularization

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Representations

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Cat

Dog

Male

Female

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Cat

Dog

Male

Female

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Cat

Dog

Male

Female

Content

Similar to ‘dress’

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Applications

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a group of young girls standing next

to each other on the beachA clock tower with a clock on top of it

A bunch of bananas hanging from a tree

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+ =

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1 0 0

1 0 0

0 1 0

0.7 -0.6 -8

0 0 1

0 0 1

0 1 0

-8 -0.6 0.7

But … not a magic bullet

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* Expensive in terms of hardware

* Expensive in terms of time

* Expensive in terms of expertise

* Expensive in terms of labelled data

* Blackbox, can’t do inference

Thanks especially to all the people I stole this content from

Questions?

@ejlbell