Subject:
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Action, 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 |
Organization:
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SW OZ DCC SMN SW OZ DCC AI SW OZ DCC CO |
Subsidient :
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NWO(Grant code :info:eu-repo/grantAgreement/NWO/Gravitation/024.001.006)
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Book title:
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Workshop on Meta-Learning (MetaLearn 2017) - Neural Information Processing Systems (NIPS 2017) |
Abstract:
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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.
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