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ECE 471/571 – Lecture 18

ECE 471/571 – Lecture 18. Use Neural Networks for Pattern Recognition – Some More Background and Recurrent Neural Network. Different Approaches - More Detail. Pattern Classification. Statistical Approach. Syntactic Approach. Supervised. Unsupervised. Basic concepts:

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ECE 471/571 – Lecture 18

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  1. ECE 471/571 – Lecture 18 Use Neural Networks for Pattern Recognition – Some More Background and Recurrent Neural Network

  2. Different Approaches - More Detail Pattern Classification Statistical Approach Syntactic Approach Supervised Unsupervised Basic concepts: Baysian decision rule (MPP, LR, Discri.) Basic concepts: Distance Agglomerative method Parametric learning (ML, BL) k-means Non-Parametric learning (kNN) Winner-take-all NN (Perceptron, BP) Kohonen maps Dimensionality Reduction Fisher’s linear discriminant K-L transform (PCA) Performance Evaluation ROC curve TP, TN, FN, FP Stochastic Methods local optimization (GD) global optimization (SA, GA) ECE471/571, Hairong Qi

  3. Definitions • According to the DARPA Neural Network Study (1988, AFCEA International Press, p. 60): • ... a neural network is a system composed of many simple processing elements operating in parallel whose function is determined by network structure, connection strengths, and the processing performed at computing elements or nodes. • According to Haykin, S. (1994), Neural Networks: A Comprehensive Foundation, NY: Macmillan, p. 2: • A neural network is a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects: • Knowledge is acquired by the network through a learning process. • Interneuron connection strengths known as synaptic weights are used to store the knowledge.

  4. Why NN? • Human brain is very good at pattern recognition and generalization • Derive meaning from complicated or imprecise data • A trained neural network can be thought of as an "expert" in the category of information it has been given to analyze. • Adaptive learning • Self-Organization • Real Time Operation • Parallel processing • Fault Tolerance • Redundancy vs. • Regeneration

  5. Key Application Areas • Identify pattern and trends in data • Examples: • Recognition of speakers in communications • Diagnosis of hepatitis • Recovery of telecommunications from faulty software • Interpretation of multimeaning Chinese words • Undersea mine detection • Texture analysis • Object recognition; handwritten word recognition; and facial recognition.

  6. http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol1/cs11/article1.htmlhttp://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol1/cs11/article1.html http://www.neurocomputing.org NN - A Bit History • First Attempts • Simple neurons which are binary devices with fixed thresholds – simple logic functions like “and”, “or” – McCulloch and Pitts (1943) • Promising & Emerging Technology • Perceptron – three layers network which can learn to connect or associate a given input to a random output - Rosenblatt (1958) • ADALINE (ADAptive LInear Element) – an analogue electronic device which uses least-mean-squares (LMS) learning rule – Widrow & Hoff (1960) • Period of Frustration & Disrepute • Minsky & Papert’s book in 1969 in which they generalized the limitations of single layer Perceptrons to multilayered systems. • “...our intuitive judgment that the extension (to multilayer systems) is sterile” • Innovation • Grossberg's (Steve Grossberg and Gail Carpenter in 1988) ART (Adaptive Resonance Theory) networks based on biologically plausible models. • Anderson and Kohonen developed associative techniques • Klopf (A. Henry Klopf) in 1972, developed a basis for learning in artificial neurons based on a biological principle for neuronal learning called heterostasis. • Werbos (Paul Werbos 1974) developed and used the back-propagation learning method • Fukushima’s (F. Kunihiko) cognitron (a step wise trained multilayered neural network for interpretation of handwritten characters). • Re-Emergence

  7. A Wrong Direction • One argument: Instead of understanding the human brain, we understand the computer. Therefore, NN dies out in 70s. • 1980s, Japan started “the fifth generation computer research project”, namely, “knowledge information processing computer system”. The project aims to improve logical reasoning to reach the speed of numerical calculation. This project proved an abortion, but it brought another climax to AI research and NN research.

  8. Biological Neuron • Dendrites: tiny fibers which carry signals to the neuron cell body • Cell body: serves to integrate the inputs from the dendrites • Axon: one cell has a single output which is axon. Axons may be very long (over a foot) • Synaptic junction: an axon impinges on a dendrite which causes input/output signal transitions

  9. http://faculty.washington.edu/chudler/chnt1.html Synapse • Communication of information between neurons is accomplished by movement of chemicals across the synapse. • The chemicals are called neurotransmitters (generated from cell body) • The neurotransmitters are released from one neuron (the presynaptic nerve terminal), then cross the synapse and are accepted by the next neuron at a specialized site (the postsynaptic receptor).

  10. The Discovery of Neurotransmitters • Otto Loewi's Experiment (1920) • Heart 1 is connected to vagus nerve, and is put in a chamber filled with saline • Electrical stimulation of vagus nerve causes heart 1 to slow down. Then after a delay, heart 2 slows down too. • Acetylcholine

  11. Action Potential • When a neurotransmitter binds to a receptor on the postsynaptic side of the synapse, it results in a change of the postsynaptic cell's excitability: it makes the postsynaptic cell either more or less likely to fire an action potential. If the number of excitatory postsynaptic events are large enough, they will add to cause an action potential in the postsynaptic cell and a continuation of the "message." • Many psychoactive drugs and neurotoxins can change the properties of neurotransmitter release, neurotransmitter reuptake and the availability of receptor binding sites.

  12. Storage of Brain • An adult nervous system possesses 1010 neurons. • With 1000 synapses per neuron, and 8 bits of storage per synapse  • 10 terabytes of storage in your brain! • Einstein’s brain • Unusually high number of glial cells in his parietal lobe (glial cells are the supporting architecture for neurons) • Extensive dendrite connectivity • Whenever anything is learned, there are new dendrite connections made between neurons

  13. ANN

  14. Types of NN • Recurrent (feedback during operation) • Hopfield • Kohonen • Associative memory • Feedforward • No feedback during operation or testing (only during determination of weights or training) • Perceptron • Back propagation

  15. Another bit of history • 1943(McCullochandPitts): • 1957 - 1962 (Rosenblatt): • From Mark I Perceptron to the Tobermory Perceptron to Perceptron Computer Simulations • Multilayer perceptronwithfixedthreshold • 1969(MinskyandPapert): • Thedarkage:70’s ~25 years • 1986(Rumelhart,Hinton,McClelland):BP • 1989(LeCunetal.):CNN(LeNet) • Another~20years • 2006 (Hinton et al.): DL • 2012(Krizhevsky,Sutskever,Hinton):AlexNet • 2014(Goodfellow,Benjo,etal.):GAN • W.S. McCulloch, W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” The Bulletin of Mathematical Biophysics, 5(4):115-133, December 1943. • F. Rosenblatt, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Spartan Books, 1962. • Minsky, S. Papert, Perceptrons: An Introduction to Computational Geometry, 1969. • D.E. Rumelhart, G.E. Hinton, R.J. Williams, “Learning representations by back-propagating errors,” Nature, 323(9):533-536, October 1986. (BP) • Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural Computation, 1(4):541-551, 1989. (LeNet). • G.E. Hinton, S. Osindero, Y. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, 18:1527-1554, 2006. (DL) • G.E. Hinton, R.R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, 313(5786):504-507, 2006 (DL) • A. Krizhevsky, I. Sutskever, G.E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems, pages 1097-1105, 2012. (AlexNet) • I.Goodfellow,J.Pouget-Abadie,M.Mirza,B.Xu,D.Warde-Farley,S.Ozair,A.Courville,Y.Bengio,“Generativeadversarialnetworks,”NIPS,2014.

  16. x1 w1 y w2 x2 …… wd xd -b 1 Why deeplearning? S1 x1 w13 S3 S w35 S5 w23 S Perceptron(40’s) w14 MLP(80’s) w45 S S4 w24 LeNet(98) x2 S2

  17. ImageNetLargeScaleVisualRecognitionChallenge(ILSVRC) Humanexpert:5.1% http://ischlag.github.io/2016/04/05/important-ILSVRC-achievements/

  18. Engineered features vs. automatic features Feature extraction Pattern classification Input media Feature vector Recognition result Need domain knowledge ECE471/571, Hairong Qi

  19. End-to-end approach? segmentation Objects & regions Image DeepLearning Description & Representation recognition Understanding, Decisions, Knowledge Features

  20. What do we cover? • Neural networks • Perceptron • MLP • Backpropagation • Feedforward networks • Supervised learning - CNN • Backpropagation • How to train more efficiently? • Issues and potential solutions • Unsupervised learning – AE • Feedback networks • RNN • GAN

  21. What’s the expectation? • ECE599 or ECE692 • Essay • Final report • http://web.utk.edu/~qi/deeplearning

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