Learned Representations
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Transcript of Learned Representations
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Learned Representations
@ejlbell
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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
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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.
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Human ingenuity and prior knowledge
Feature Engineering
Representation Learning
Sufficiently powerful models that learn “good” feature transforms
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Deep Learning
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Image Filters
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VGG
Representations
2015 - Very Deep Convolutional Networks for
Large-Scale Image Recognition. Simonyan and
Zisserman
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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
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Content
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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
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A bunch of bananas hanging from a tree
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+ =
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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
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Thanks especially to all the people I stole this content from
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Questions?
@ejlbell