A Near-Optimal Gradient Flow for Learning Neural Energy-Based Models

31 October 2019

Papers citing "A Near-Optimal Gradient Flow for Learning Neural Energy-Based Models"

10 / 10 papers shown

Title
Score-Based Generative Modeling through Stochastic Differential Equations Yang Song Jascha Narain Sohl-Dickstein Diederik P. Kingma Abhishek Kumar Stefano Ermon Ben Poole DiffM SyDa 330 6,453 0 26 Nov 2020
Improved Techniques for Training Score-Based Generative Models Yang Song Stefano Ermon DiffM 218 1,151 0 16 Jun 2020
Training Generative Adversarial Networks with Limited Data Tero Karras M. Aittala Janne Hellsten S. Laine J. Lehtinen Timo Aila GAN 146 1,884 0 11 Jun 2020
Generative Modeling by Estimating Gradients of the Data Distribution Yang Song Stefano Ermon SyDa DiffM 230 3,902 0 12 Jul 2019
A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras S. Laine Timo Aila 560 10,551 0 12 Dec 2018
"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors Yossi Gandelsman Assaf Shocher Michal Irani 66 310 0 02 Dec 2018
Learning Energy-Based Models as Generative ConvNets via Multi-grid Modeling and Sampling Ruiqi Gao Yang Lu Junpei Zhou Song-Chun Zhu Ying Nian Wu 74 79 0 26 Sep 2017
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 65 1,092 0 16 Aug 2016
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Alec Radford Luke Metz Soumith Chintala GAN OOD 250 14,008 0 19 Nov 2015
MCMC using Hamiltonian dynamics Radford M. Neal 290 3,281 0 09 Jun 2012