Transcript part 1 pptx
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 and hidden->output connections, you could learn 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 passed back 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 Combines An Encoder And A Decoder Decoder Encoder Stacked Autoencoders ... copy deep network Note that decoders can be stacked to produce a generative model of the domain Neural Net Can Be Viewed As A Graphical Model y x1 x2 x3 x4 Deterministic neuron ìï 1 if y = (1+ exp(- w x ))-1 å i i P(y | x1, x2 , x3 , x4 ) = í otherwise ïî 0 Stochastic neuron ìï 1 with probability (1+ exp(- w x ))-1 å ii P(y | x1, x2 , x3 , x4 ) = í otherwise ïî 0 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 for doing inference for special cases of the architecture. E.g., Restricted Boltzmann Machine 2 layers Completely interconnected between layers 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 prefer different neural substrate, but it doesn’t really matter if you use deterministic neural net vs. RBM erent Levels of Abstraction cal Learning gression from low level structure as ural complexity onitor what is being o guide the machine bspaces er level on can be used for ct tasks From Ng’s group 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 Bharatiya Fernall ’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 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