Deep Learning: Back To The Future
Hinton NIPS 2012 Talk Slide (More Or Less)
What was hot in 1987 Neural networks
What happened in ML since 1987 Computers got faster Larger data sets became available
What is hot 25 years later Neural networks
… but they are informed by graphical models!
Brief History Of Machine Learning
1960s Perceptrons
1969 Minsky & Papert book
1985-1995 Neural Nets and Back Propagation
1995- Support-Vector Machines
2000- Bayesian Models
2013- Deep Networks
What My Lecture
Looked Like In 1987
The Limitations Of Two Layer Networks
Many problems can’t be learned without a layer of intermediate or hidden units.
Problem Where does training signal come from?Teacher specifies target outputs, not target hidden unit activities.
If you could learn input->hidden andhidden->output connections, you couldlearn new representations!
But how do hidden units get an error signal?
Why Stop At One Hidden Layer?
E.g., vision hierarchy for recognizing handprinted text
Word output layer Character hidden layer 3 Stroke hidden layer 2 Edge hidden layer 1 Pixel input layer
Demos
Yann LeCun’s LeNet5 http://yann.lecun.com/exdb/lenet/index.html
Why Deeply Layered Networks Fail
Credit assignment problem How is a neuron in layer 2 supposed to know what it should output until all the neurons above it do something sensible?
How is a neuron in layer 4 supposed to know what it should output until all the neurons below it do something sensible?
Mathematical manifestation Error gradients get squashed as they are passedback through a deep network
Solution
Traditional method of training Random initial weights
Alternative Do unsupervised learning layer by layer to get weights in a sensible configuration for the statistics of the input.
Then when net is trained in a supervised fashion, credit assignment will be easier.
Autoencoder Networks Self-supervised training procedure
Given a set of input vectors (no target outputs)
Map input back to itself via a hidden layer bottleneck
How to achieve bottleneck?
Fewer neurons
Sparsity constraint
Information transmission constraint (e.g., add noise to unit, or shut off randomly, a.k.a. dropout)
Autoencoder CombinesAn Encoder And A Decoder
Encoder
Decoder
Stacked Autoencoders
Note that decoders can be stacked to produce a generative model of the domain
copy
...
deep network
Neural Net Can BeViewed As A Graphical Model
Deterministic neuron
Stochastic neuron
x1 x2 x4x3
y
Boltzmann Machine(Hinton & Sejnowski, circa 1985)
Undirected graphical model
Each node is a stochastic neuron
Potential function defined on each pair of neurons
Algorithms were developed fordoing inference for special casesof the architecture.
E.g., Restricted Boltzmann Machine
2 layers
Completely interconnected betweenlayers
No connections within layer
Punch Line
Deep network can be implemented as a multilayer restricted Boltzmann machine
Sequential layer-to-layer training procedure Training requires probabilistic inference Update rule: ‘contrastive divergence’
Different research groups preferdifferent neural substrate, but itdoesn’t really matter if you usedeterministic neural net vs. RBM
From Ng’s group
Suskever, Martens, Hinton (2011)Generating Text From A Deep Belief Net
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2013 News
No need to use unsupervised training or probabilistic models if…
You use clever tricks of the neural net trade, i.e.,
Back propagation with
deep networks
rectified linear units
dropout
weight maxima
Krizhevsky, Sutskever, & Hinton
ImageNet competition
15M images in 22k categories
For contest, 1.2M images in 1k categories
Classification: can you name object in 5 guesses?
√
2012 Results
2013: Down to 11% error