ICML 2017poster10 citations
Canopy Fast Sampling with Cover Trees
Manzil Zaheer, Satwik Kottur, Amr Ahmed, José Moura, Alex Smola
Abstract
Hierarchical Bayesian models often capture distributions over a very large number of distinct atoms. The need for these models arises when organizing huge amount of unsupervised data, for instance, features extracted using deep convnets that can be exploited to organize abundant unlabeled images. Inference for hierarchical Bayesian models in such cases can be rather nontrivial, leading to approximate approaches. In this work, we propose
BibTeX
@InProceedings{pmlr-v70-zaheer17b,
title = {Canopy Fast Sampling with Cover Trees},
author = {Manzil Zaheer and Satwik Kottur and Amr Ahmed and Jos{\'e} Moura and Alex Smola},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {3977--3986},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/zaheer17b/zaheer17b.pdf},
url = {https://proceedings.mlr.press/v70/zaheer17b.html},
abstract = {Hierarchical Bayesian models often capture distributions over a very large number of distinct atoms. The need for these models arises when organizing huge amount of unsupervised data, for instance, features extracted using deep convnets that can be exploited to organize abundant unlabeled images. Inference for hierarchical Bayesian models in such cases can be rather nontrivial, leading to approximate approaches. In this work, we propose