Not All Samples Are Created Equal: Deep Learning with Importance Sampling

2 March 2018

Papers citing "Not All Samples Are Created Equal: Deep Learning with Importance Sampling"

7 / 107 papers shown

Title
FIS-GAN: GAN with Flow-based Importance Sampling Shiyu Yi Donglin Zhan Wenqing Zhang Zhengyang Geng Kang An Hao Wang GAN 27 3 0 06 Oct 2019
Empirical study towards understanding line search approximations for training neural networks Younghwan Chae D. Wilke 27 11 0 15 Sep 2019
Faster Neural Network Training with Data Echoing Dami Choi Alexandre Passos Christopher J. Shallue George E. Dahl 23 48 0 12 Jul 2019
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization Kenji Kawaguchi Haihao Lu ODL 30 62 0 09 Jul 2019
Submodular Batch Selection for Training Deep Neural Networks K. J. Joseph R. VamshiTeja Krishnakant Singh V. Balasubramanian 13 23 0 20 Jun 2019
Natural Compression for Distributed Deep Learning Samuel Horváth Chen-Yu Ho L. Horvath Atal Narayan Sahu Marco Canini Peter Richtárik 21 151 0 27 May 2019
An Empirical Study of Example Forgetting during Deep Neural Network Learning Mariya Toneva Alessandro Sordoni Rémi Tachet des Combes Adam Trischler Yoshua Bengio Geoffrey J. Gordon 46 715 0 12 Dec 2018