Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training

22 June 2021

Papers citing "Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training"

4 / 4 papers shown

Title
OpenNMT: Open-Source Toolkit for Neural Machine Translation Guillaume Klein Yoon Kim Yuntian Deng Jean Senellart Alexander M. Rush 273 1,896 0 10 Jan 2017
Effective Approaches to Attention-based Neural Machine Translation Thang Luong Hieu H. Pham Christopher D. Manning 218 7,926 0 17 Aug 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,145 0 06 Jun 2015
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 266 7,638 0 03 Jul 2012