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Minimum Probability Flow Learning

25 June 2009

Jascha Narain Sohl-Dickstein

P. Battaglino

M. DeWeese

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Papers citing "Minimum Probability Flow Learning"

31 / 31 papers shown

Title
Covariance Density Neural Networks Om Roy Yashar Moshfeghi Keith Smith BDL 97 0 0 16 May 2025
Informed Correctors for Discrete Diffusion Models Yixiu Zhao Jiaxin Shi F. Chen Shaul Druckmann Lester W. Mackey Scott W. Linderman 130 15 0 30 Jul 2024
Fast Sampling via Discrete Non-Markov Diffusion Models Zixiang Chen Huizhuo Yuan Yongqian Li Yiwen Kou Junkai Zhang Quanquan Gu DiffM 93 7 0 14 Dec 2023
Discrete Langevin Sampler via Wasserstein Gradient Flow Haoran Sun H. Dai Bo Dai Haomin Zhou Dale Schuurmans BDL 90 24 0 29 Jun 2022
Gradient Estimation with Discrete Stein Operators Jiaxin Shi Yuhao Zhou Jessica Hwang Michalis K. Titsias Lester W. Mackey 101 23 0 19 Feb 2022
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion Tim Dockhorn Arash Vahdat Karsten Kreis DiffM 108 236 0 14 Dec 2021
Stein's Method Meets Computational Statistics: A Review of Some Recent Developments Andreas Anastasiou Alessandro Barp F. Briol B. Ebner Robert E. Gaunt ... Qiang Liu Lester W. Mackey Chris J. Oates Gesine Reinert Yvik Swan 101 35 0 07 May 2021
How to Train Your Energy-Based Models Yang Song Diederik P. Kingma DiffM 95 265 0 09 Jan 2021
Contrastive Divergence Learning is a Time Reversal Adversarial Game Omer Yair T. Michaeli GAN 67 6 0 06 Dec 2020
Bi-level Score Matching for Learning Energy-based Latent Variable Models Fan Bao Chongxuan Li Kun Xu Hang Su Jun Zhu Bo Zhang 76 14 0 15 Oct 2020
A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models Ziyu Wang Shuyu Cheng Yueru Li Jun Zhu Bo Zhang 67 14 0 18 Feb 2020
Biologically Plausible Sequence Learning with Spiking Neural Networks Zuozhu Liu Thiparat Chotibut Christopher Hillar Shaowei Lin 13 2 0 25 Nov 2019
Energy-Inspired Models: Learning with Sampler-Induced Distributions Dieterich Lawson George Tucker Bo Dai Rajesh Ranganath 90 31 0 31 Oct 2019
Generative Modeling by Estimating Gradients of the Data Distribution Yang Song Stefano Ermon SyDa DiffM 260 3,964 0 12 Jul 2019
Minimum Stein Discrepancy Estimators Alessandro Barp François‐Xavier Briol Andrew B. Duncan Mark Girolami Lester W. Mackey 80 93 0 19 Jun 2019
Exponential Family Estimation via Adversarial Dynamics Embedding Bo Dai Ziqiang Liu H. Dai Niao He Arthur Gretton Le Song Dale Schuurmans 82 53 0 27 Apr 2019
A Unified Dynamic Approach to Sparse Model Selection Chendi Huang Yuan Yao 21 7 0 08 Oct 2018
Variational Probability Flow for Biologically Plausible Training of Deep Neural Networks Zuozhu Liu Tony Q.S. Quek Shaowei Lin 26 3 0 21 Nov 2017
On better training the infinite restricted Boltzmann machines Xuan Peng Xunzhang Gao Xiang Li AI4CE 33 15 0 11 Sep 2017
Transport Analysis of Infinitely Deep Neural Network Sho Sonoda Noboru Murata 34 4 0 10 May 2016
Neural Autoregressive Distribution Estimation Benigno Uria Marc-Alexandre Côté Karol Gregor Iain Murray Hugo Larochelle 94 314 0 07 May 2016
Partition Functions from Rao-Blackwellized Tempered Sampling David Carlson Patrick Stinson Ari Pakman Liam Paninski 120 13 0 07 Mar 2016
A note on the evaluation of generative models Lucas Theis Aaron van den Oord Matthias Bethge EGVM 158 1,147 0 05 Nov 2015
A Deep Embedding Model for Co-occurrence Learning Yelong Shen R. Jin Jianshu Chen Xiaodong He Jianfeng Gao Li Deng 18 5 0 11 Apr 2015
Deep Unsupervised Learning using Nonequilibrium Thermodynamics Jascha Narain Sohl-Dickstein Eric A. Weiss Niru Maheswaranathan Surya Ganguli SyDa DiffM 315 7,040 0 12 Mar 2015
Understanding Minimum Probability Flow for RBMs Under Various Kinds of Dynamics Daniel Jiwoong Im Ethan Buchman Graham W. Taylor 98 2 0 20 Dec 2014
Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods Jascha Narain Sohl-Dickstein Ben Poole Surya Ganguli ODL 172 124 0 09 Nov 2013
Efficient Methods for Unsupervised Learning of Probabilistic Models Jascha Narain Sohl-Dickstein TPM 55 0 0 19 May 2012
Hamiltonian Annealed Importance Sampling for partition function estimation Jascha Narain Sohl-Dickstein B. J. Culpepper 136 38 0 09 May 2012
The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use Jascha Narain Sohl-Dickstein 125 11 0 08 May 2012
In All Likelihood, Deep Belief Is Not Enough Lucas Theis S. Gerwinn Fabian H. Sinz Matthias Bethge 109 39 0 28 Nov 2010