Learning Gradient Descent: Better Generalization and Longer Horizons

10 March 2017

Papers citing "Learning Gradient Descent: Better Generalization and Longer Horizons"

11 / 61 papers shown

Title
Improved Adversarial Training via Learned Optimizer Yuanhao Xiong Cho-Jui Hsieh AAML 28 30 0 25 Apr 2020
Learning to be Global Optimizer Haotian Zhang Jianyong Sun Zongben Xu 6 3 0 10 Mar 2020
Using a thousand optimization tasks to learn hyperparameter search strategies Luke Metz Niru Maheswaranathan Ruoxi Sun C. Freeman Ben Poole Jascha Narain Sohl-Dickstein 20 45 0 27 Feb 2020
Pyramid Convolutional RNN for MRI Image Reconstruction Eric Z. Chen Puyang Wang Xiao Chen Terrence Chen Shanhui Sun 13 41 0 02 Dec 2019
Learning to Learn by Zeroth-Order Oracle Yangjun Ruan Yuanhao Xiong Sashank J. Reddi Sanjiv Kumar Cho-Jui Hsieh 22 17 0 21 Oct 2019
ROAM: Recurrently Optimizing Tracking Model Tianyu Yang Pengfei Xu Runbo Hu Hua Chai Antoni B. Chan 6 92 0 28 Jul 2019
HyperAdam: A Learnable Task-Adaptive Adam for Network Training Shipeng Wang Jian Sun Zongben Xu ODL 25 22 0 22 Nov 2018
Understanding and correcting pathologies in the training of learned optimizers Luke Metz Niru Maheswaranathan Jeremy Nixon C. Freeman Jascha Narain Sohl-Dickstein ODL 39 149 0 24 Oct 2018
Guided evolutionary strategies: Augmenting random search with surrogate gradients Niru Maheswaranathan Luke Metz George Tucker Dami Choi Jascha Narain Sohl-Dickstein 22 20 0 26 Jun 2018
Meta Continual Learning Risto Vuorio D.-Y. Cho Daejoong Kim Jiwon Kim CLL 11 28 0 11 Jun 2018
Understanding Short-Horizon Bias in Stochastic Meta-Optimization Yuhuai Wu Mengye Ren Renjie Liao Roger C. Grosse 22 138 0 06 Mar 2018