Results 281 to 290 of about 2,281,376 (313)
Application of stacked bidirectional LSTM neural networks in reservoir porosity prediction. [PDF]
Sci RepZhang P, Hu S, Xiao Y, Chen P, Sun D.
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Neuromorphic computing paradigms enhance robustness through spiking neural networks. [PDF]
Nat CommunDing J, Yu Z, Liu JK, Huang T.
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2009
Neural networks are a class of intelligent learning machines establishing the relationships between descriptors of real-world objects. As optimisation tools they are also a class of computational algorithms implemented using statistical/numerical techniques for parameter estimate, model selection, and generalisation enhancement.
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Neural networks are a class of intelligent learning machines establishing the relationships between descriptors of real-world objects. As optimisation tools they are also a class of computational algorithms implemented using statistical/numerical techniques for parameter estimate, model selection, and generalisation enhancement.
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Physical Review E, 1993
We propose a class of network models suited for negative-choice classification.
, Nowak, , Lewenstein, , Tarkowski
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We propose a class of network models suited for negative-choice classification.
, Nowak, , Lewenstein, , Tarkowski
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2007
Some of the (comparatively older) numerical results on neural network models obtained by our group are reviewed. These models incorporate synaptic connections constructed by using the Hebb's rule. The dynamics is determined by the internal field which has a weighted contribution from the time delayed signals.
Bikas K, Chakrabarti, Abhik, Basu
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Some of the (comparatively older) numerical results on neural network models obtained by our group are reviewed. These models incorporate synaptic connections constructed by using the Hebb's rule. The dynamics is determined by the internal field which has a weighted contribution from the time delayed signals.
Bikas K, Chakrabarti, Abhik, Basu
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Annual Review of Physiology, 1985
Despite the fact that a large number of neuronal oscillators have been described, there are only a few good examples that illustrate how they operate at the cellular level. For most, there is some isolated information about different aspects of the oscillator network, but too little to explain the whole mechanism.
A I, Selverston, M, Moulins
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Despite the fact that a large number of neuronal oscillators have been described, there are only a few good examples that illustrate how they operate at the cellular level. For most, there is some isolated information about different aspects of the oscillator network, but too little to explain the whole mechanism.
A I, Selverston, M, Moulins
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2021
In this section, we will see how to train a neural network model in the Wolfram Language, how to access the results, and the trained network. We will review the basic commands to export and import a net model. We end the chapter with the exploration of the Wolfram Neural Net Repository and the review of the LeNet network model.
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In this section, we will see how to train a neural network model in the Wolfram Language, how to access the results, and the trained network. We will review the basic commands to export and import a net model. We end the chapter with the exploration of the Wolfram Neural Net Repository and the review of the LeNet network model.
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Biological Cybernetics, 1991
The neural network that efficiently and nearly optimally solves difficult optimization problems is defined. The convergence proof for the Markovian neural network that asynchronously updates its neurons' states is also presented. The comparison of the performance of the Markovian neural network with various combinatorial optimization methods in two ...
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The neural network that efficiently and nearly optimally solves difficult optimization problems is defined. The convergence proof for the Markovian neural network that asynchronously updates its neurons' states is also presented. The comparison of the performance of the Markovian neural network with various combinatorial optimization methods in two ...
openaire +3 more sources