Optimization algorithm for artificial neural networks.
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The leads are taking indications of interest at 30bp area over mid-swaps.
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The books are open at guidance heard at mid-swaps less 6bp area.
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That put the new issue concession on the three-year at about 18bp.
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The note was initially marketed at 45bp area over three month Euribor.
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The issuer is marketing a five-year deal at 205bp area over mid-swaps.
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This makes backpropagation-orsimply backprop-theconnectionists' master algorithm.
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Combining connectionism and evolutionism was fairly easy: just evolve the network structure and learn the parameters by backpropagation.
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To train SNNs with supervision, we propose an efficient on-chip training scheme approximating backpropagation algorithm suitable for hardware implementation.
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The connectionists' master algorithm is backpropagation, which they use to figure out which neurons are responsible for which errors and adjust their weights accordingly.
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The aim of this study is to develop backpropagation neural networks (BPNN) for better prediction of ventilatory function in children and adolescents.
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Beware of attaching too much meaning to the weights backprop finds, however.
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That's what happens to backprop, except it climbs mountains in hyperspace instead of 3-D.
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Then learn the connection weights using backprop, and your brand-new brain is ready to use.
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And with backprop we can learn the appropriate weights, resulting in a successful Nike prospect detector.
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This makes backpropagation-orsimply backprop-theconnectionists' master algorithm.
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So does backprop solve the machine-learning problem?
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Beyond that, backprop broke down.
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Some connectionists have been overheard claiming that backprop is the Master Algorithm and we just need to scale it up.
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Among other things, they showed that backprop can learn XOR, enabling connectionists to thumb their noses at Minsky and Papert.
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In an early demonstration of the power of backprop, Terry Sejnowski and Charles Rosenberg trained a multilayer perceptron to read aloud.
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In those days researchers didn't trust computer simulations; they demanded mathematical proof that an algorithm would work, and there's no such proof for backprop.
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The Master Algorithm is neither genetic programming nor backprop, but it has to include the key elements of both: structure learning and weight learning.
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When backprop first hit the streets, connectionists had visions of quickly learning larger and larger networks until, hardware permitting, they amounted to artificial brains.
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But if it starts at 5.5, on the other hand, backprop will roll down to 7.0 and remain stuck there.
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Backprop proceeds deterministically after setting the initial weights to small random values.
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Backprop learns weights for a predefined network architecture; denser networks are more flexible but also harder to learn.
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