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A Universal Marginalizer for Amortized Inference in Generative Models

Douglas, L; Zarov, I; Gourgoulias, K; Lucas, C; Hart, C; Baker, A; Sahani, M; ... Johri, S; + view all (2017) A Universal Marginalizer for Amortized Inference in Generative Models. In: Proceedings of 31st Conference on Neural Information Processing Systems (NIPS 2017),. NIPS: Long Beach, CA, USA. Green open access

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Abstract

We consider the problem of inference in a causal generative model where the set of available observations differs between data instances. We show how combining samples drawn from the graphical model with an appropriate masking function makes it possible to train a single neural network to approximate all the corresponding conditional marginal distributions and thus amortize the cost of inference. We further demonstrate that the efficiency of importance sampling may be improved by basing proposals on the output of the neural network. We also outline how the same network can be used to generate samples from an approximate joint posterior via a chain decomposition of the graph.

Type: Proceedings paper
Title: A Universal Marginalizer for Amortized Inference in Generative Models
Event: 31st Conference on Neural Information Processing Systems (NIPS 2017),
Location: Long Beach, USA
Dates: 8 December 2017
Open access status: An open access version is available from UCL Discovery
Publisher version: http://approximateinference.org/2017/accepted/Doug...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: http://discovery.ucl.ac.uk/id/eprint/10038796
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