Results 221 to 230 of about 1,242,716 (272)
Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO<sub>2</sub> Emissions. [PDF]
Environ Sci TechnolJeong S +5 more
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Gaussian Process Regression for Mapping Free EnergyLandscape of Mg<sup>2+</sup>-Cl<sup>-</sup> Ion Pairing in Aqueous Solution: Molecular Insights and Computational Efficiency. [PDF]
MoleculesPornpatcharapong W.
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Predicting hydrogen atom transfer energy barriers using Gaussian process regression.
Digit DiscovUlanov E +4 more
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IEEE Transactions on Neural Networks, 2011
Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of ...
Demiris, Yiannis, Chatzis, Sotirios P.
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Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of ...
Demiris, Yiannis, Chatzis, Sotirios P.
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Acta Mathematicae Applicatae Sinica, 1994
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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2005
Abstract We return in this chapter to the general Bayesian formalism for a single model. So far we have worked out everything in terms of the posterior distribution p(w D) of the model parameters w, given the data D; to get predictions, we need to integrate over this distribution.
A C C Coolen, R Kühn, P Sollich
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Abstract We return in this chapter to the general Bayesian formalism for a single model. So far we have worked out everything in terms of the posterior distribution p(w D) of the model parameters w, given the data D; to get predictions, we need to integrate over this distribution.
A C C Coolen, R Kühn, P Sollich
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1999
Abstract This chapter studies some examples of processes that are Gaussian or conditionally Gaussian.
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Abstract This chapter studies some examples of processes that are Gaussian or conditionally Gaussian.
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Gaussian Process Regression for Materials and Molecules
Chemical Reviews, 2021Volker L Deringer +2 more
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