Hopfieldwpics

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Neural Networks - Hopfield Relaxation and Hopfield Networks Neural Networks
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Neural Networks - Hopfield Bibliography Hopfield, J. J., "Neural networks and physical systems with emergent collective computational abilities," Proceedings of the National Academy of Sciences 79 :2554-2558, 1982. Hopfield, J. J., "Neurons with graded response have collective computational properties like those of two-state neurons." Proceedings of the National Academy of Sciences 81: 3088-3092, 1984. Abu-Mostafa, and J. St. Jacques, Information Capacity of the Hopfield Model, IEEE Trans. on Information Theory , Vol. IT-31, No. 4, 1985.
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Neural Networks - Hopfield Limited by Lower order constraints Has no hidden nodes, higher order units All nodes visible i.e. Program as CAM 0 0 0
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Unformatted text preview: 0 1 1 1 0 1 1 1 0 However, relaxing auto-association allows a garbled input to return a clean output Assume two patterns trained A -> X B -> Y Now enter the example with .6A and .4B Result in a Backprop model? Result in the Hopfield autoassociator: X Neural Networks - Hopfield Hopfield as a Computation Engine Optimization Travelling Salesman Problem (TSP) NP-Complete "Good" vs. Optimal Solutions Very Fast Processing Neural Networks - Hopfield Neural Networks - Hopfield Derive Energy equation for TSP 1. Legal State 2. Good State Set weights accordingly How would we do it Neural Networks - Hopfield Neural Networks - Hopfield Neural Networks - Hopfield Neural Networks - Hopfield Summary Much Current Work Saturation and No Convergence For Optimization, Saturation is moot Many important Optimization problems Non learning, but reasonably intuitive programming - extensions to learning Highly Parallel Expensive Interconnect Lots of Physical Implementation work, optics...
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