ICML 2017poster161 citations
Attentive Recurrent Comparators
Pranav Shyam, Shubham Gupta, Ambedkar Dukkipati
Abstract
Rapid learning requires flexible representations to quickly adopt to new evidence. We develop a novel class of models called Attentive Recurrent Comparators (ARCs) that form representations of objects by cycling through them and making observations. Using the representations extracted by ARCs, we develop a way of approximating a
BibTeX
@InProceedings{pmlr-v70-shyam17a,
title = {Attentive Recurrent Comparators},
author = {Pranav Shyam and Shubham Gupta and Ambedkar Dukkipati},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {3173--3181},
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/shyam17a/shyam17a.pdf},
url = {https://proceedings.mlr.press/v70/shyam17a.html},
abstract = {Rapid learning requires flexible representations to quickly adopt to new evidence. We develop a novel class of models called Attentive Recurrent Comparators (ARCs) that form representations of objects by cycling through them and making observations. Using the representations extracted by ARCs, we develop a way of approximating a