Bayesian model ensembling using meta-trained recurrent neural networks
[S.l. : s.n.]
InWorkshop on Meta-Learning (MetaLearn 2017) - Neural Information Processing Systems (NIPS 2017), pp. 1-5
31st Conference on Neural Information Processing Systems (NIPS 2017) (Long Beach, CA, USA, December 4-9, 2017)
Article in monograph or in proceedings
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SW OZ DCC SMN
SW OZ DCC AI
SW OZ DCC CO
Workshop on Meta-Learning (MetaLearn 2017) - Neural Information Processing Systems (NIPS 2017)
SubjectAction, intention, and motor control; Cognitive artificial intelligence; DI-BCB_DCC_Theme 2: Perception, Action and Control; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication; Language in Interaction
In this paper we demonstrate that a recurrent neural network meta-trained on an ensemble of arbitrary classification tasks can be used as an approximation of the Bayes optimal classifier. This result is obtained by relying on the framework of e-free approximate Bayesian inference, where the Bayesian posterior is approximated by training a neural network using synthetic samples. We denote the resulting model as neural ensembler. We show that a single neural ensembler trained on a large set of synthetic data achieves competitive classification performance on multiple real-world classification problems without additional training.
NWO (Grant code:info:eu-repo/grantAgreement/NWO/Gravitation/024.001.006)
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