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.
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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 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: | https://discovery.ucl.ac.uk/id/eprint/10038796 |
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