Neural Permutation Processes

Ari Pakman, Yueqi Wang, Liam Paninski
Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference, PMLR 118:1-7, 2020.

Abstract

We introduce a neural architecture to perform amortized approximate Bayesian inference over latent random permutations of two sets of objects. The method involves approximating permanents of matrices of pairwise probabilities using recent ideas on functions dened over sets. Each sampled permutation comes with a probability estimate, a quantity unavailable in MCMC approaches. We illustrate the method in sets of 2D points and MNIST images.

Cite this Paper

BibTeX
@InProceedings{pmlr-v118-pakman20a, title = {Neural Permutation Processes }, author = {Pakman, Ari and Wang, Yueqi and Paninski, Liam}, booktitle = {Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference}, pages = {1--7}, year = {2020}, editor = {Zhang, Cheng and Ruiz, Francisco and Bui, Thang and Dieng, Adji Bousso and Liang, Dawen}, volume = {118}, series = {Proceedings of Machine Learning Research}, month = {08 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v118/pakman20a/pakman20a.pdf}, url = {https://proceedings.mlr.press/v118/pakman20a.html}, abstract = { We introduce a neural architecture to perform amortized approximate Bayesian inference over latent random permutations of two sets of objects. The method involves approximating permanents of matrices of pairwise probabilities using recent ideas on functions dened over sets. Each sampled permutation comes with a probability estimate, a quantity unavailable in MCMC approaches. We illustrate the method in sets of 2D points and MNIST images.} }
Endnote
%0 Conference Paper %T Neural Permutation Processes %A Ari Pakman %A Yueqi Wang %A Liam Paninski %B Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference %C Proceedings of Machine Learning Research %D 2020 %E Cheng Zhang %E Francisco Ruiz %E Thang Bui %E Adji Bousso Dieng %E Dawen Liang %F pmlr-v118-pakman20a %I PMLR %P 1--7 %U https://proceedings.mlr.press/v118/pakman20a.html %V 118 %X We introduce a neural architecture to perform amortized approximate Bayesian inference over latent random permutations of two sets of objects. The method involves approximating permanents of matrices of pairwise probabilities using recent ideas on functions dened over sets. Each sampled permutation comes with a probability estimate, a quantity unavailable in MCMC approaches. We illustrate the method in sets of 2D points and MNIST images.
APA
Pakman, A., Wang, Y. & Paninski, L.. (2020). Neural Permutation Processes . Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference, in Proceedings of Machine Learning Research 118:1-7 Available from https://proceedings.mlr.press/v118/pakman20a.html.

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