Deep Learning: Back To The Future

1. Deep Learning: Back To The Future

2. 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!

3. 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

4. What My Lecture Looked Like In 1987

13. 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?

17. 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

18. Demos • YannLeCun’s LeNet5 • http://yann.lecun.com/exdb/lenet/index.html

19. 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

20. 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.

21. 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)

22. Autoencoder CombinesAn Encoder And A Decoder Decoder Encoder

23. Stacked Autoencoders ... • Note that decoders can be stacked to produce a generative model of the domain copy deep network

24. Neural Net Can BeViewed As A Graphical Model • Deterministic neuron • Stochastic neuron y x2 x4 x1 x3

25. 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

26. 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

27. From Ng’s group

28. Suskever, Martens, Hinton (2011)Generating Text From A Deep Belief Net • Wikipedia • The meaning of life is the tradition of the ancient human reproduction: it is less favorable to the good boy for when to remove her bigger. In the show’s agreement unanimously resurfaced. The wild pasteured with consistent street forests were incorporated by the 15th century BE. In 1996 the primary rapford undergoes an effort that the reserve conditioning, written into Jewish cities, sleepers to incorporate the .St Eurasia that activates the population. Mar??a Nationale, Kelli, Zedlat-Dukastoe, Florendon, Ptu’s thought is. To adapt in most parts of North America, the dynamic fairy Dan please believes, the free speech are much related to the • NYT • while he was giving attention to the second advantage of school building a 2-for-2 stool killed by the Cultures saddled with a half- suit defending the BharatiyaFernall ’s office . Ms . Claire Parters will also have a history temple for him to raise jobs until naked Prodiena to paint baseball partners , provided people to ride both of Manhattan in 1978 , but what was largely directed to China in 1946 , focusing on the trademark period is the sailboat yesterday and comments on whom they obtain overheard within the 120th anniversary , where many civil rights defined , officials said early that forms , ” said Bernard J. Marco Jr. of Pennsylvania , was monitoring New York

29. 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

30. 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?

31. √

33. 2012 Results 2013: Down to 11% error