@article{munkhdalai2017learning,
author = {Munkhdalai, Tsendsuren and Yuan, Xingdi and Mehri, Soroush and Wang, Tong and Trischler, Adam},
title = {Learning Rapid-Temporal Adaptations},
year = {2017},
month = {December},
abstract = {A hallmark of human intelligence and cognition is its flexibility. One of the long-standing goals in AI research is to replicate this flexibility in a learning machine. In this work we describe a mechanism by which artificial neural networks can learn rapid-temporal adaptation - the ability to adapt quickly to new environments or tasks - that we call adaptive neurons. Adaptive neurons modify their activations with task-specific values retrieved from a working memory. On standard metalearning and few-shot learning benchmarks in both vision and language domains, models augmented with adaptive neurons achieve state-of-the-art results.},
url = {http://approjects.co.za/?big=en-us/research/publication/learning-rapid-temporal-adaptations/},
pages = {10},
journal = {Machine Learning},
}