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Bayesian inference for group-level cortical surface image-on-scalar regression with Gaussian process priors. [PDF]
BiometricsWhiteman AS, Johnson TD, Kang J.
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Predicting hydrogen atom transfer energy barriers using Gaussian process regression.
Digit DiscovUlanov E +4 more
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Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
Hung Tran-The +3 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
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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
openaire +1 more source