Fetching the paper…
Reading the bibliography…
Markov Chain Monte Carlo (MCMC) methods sample from unnormalized probability distributions and offer guarantees of exact sampling.
Monte carlo sampling methods using markov chains and their applications
W.K. Hastings · 1970
Earlier work this paper cites.
Probabilistic inference using Markov chain Monte Carlo methods
Radford M Neal · 1993
Earlier work this paper cites.
Slice sampling
Radford M Neal · 2003
Earlier work this paper cites.
Training restricted boltzmann machines using approximations to the likelihood gradient
Tijmen Tieleman · 2008
Earlier work this paper cites.
Adaptively scaling the metropolis algorithm using expected squared jumped distance
Cristian Pasarica and Andrew Gelman · 2010
Earlier work this paper cites.
Riemann manifold langevin and hamiltonian monte carlo methods
Mark Girolami and Ben Calderhead · 2011
Earlier work this paper cites.
Mcmc using hamiltonian dynamics
Radford M Neal et al · 2011
Earlier work this paper cites.
A general metric for riemannian manifold hamiltonian monte carlo
Michael Betancourt · 2013
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
Earlier work this paper cites.
The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo
Matthew D Hoffman and Andrew Gelman · 2014
Earlier work this paper cites.
Hamiltonian monte carlo without detailed balance
Jascha Sohl-Dickstein, Mayur Mudigonda, and Michael R DeWeese · 2014
Earlier work this paper cites.
Markov chain monte carlo and variational inference: Bridging the gap
Tim Salimans, Diederik Kingma, and Max Welling · 2015
Earlier work this paper cites.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
Earlier work this paper cites.
An introduction to sampling via measure transport
Youssef Marzouk, Tarek Moselhy, Matthew Parno, and Alessio Spantini · 2016
Cited alongside, same era.
The geometric foundations of hamiltonian monte carlo
Michael Betancourt, Simon Byrne, Sam Livingstone, Mark Girolami, et al · 2017
Cited alongside, same era.
Learning deep latent gaussian models with markov chain monte carlo
Matthew D Hoffman · 2017
Cited alongside, same era.
A-nice-mc: Adversarial training for mcmc
Jiaming Song, Shengjia Zhao, and Stefano Ermon · 2017
Cited alongside, same era.
Generalizing hamiltonian monte carlo with neural networks
Daniel Levy, Matt D Hoffman, and Jascha Sohl-Dickstein · 2018
Cited alongside, same era.
Metropolis-hastings view on variational inference and adversarial training
On the anatomy of mcmc-based maximum likelihood learning of energy-based models
Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, and Ying Nian Wu · 2019
Later among the works it cites.
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
Later among the works it cites.
Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2019
Later among the works it cites.
On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron van den Oord, Alexander A Alemi, and George Tucker · 2019
Later among the works it cites.
Understanding the limitations of variational mutual information estimators
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kirill Neklyudov, Evgenii Egorov, Pavel Shvechikov, and Dmitry Vetrov · 2018
Cited alongside, same era.
Ergodic measure preserving flows
Yichuan Zhang, José Miguel Hernández-Lobato, and Zoubin Ghahramani · 2018
Cited alongside, same era.
Implicit generation and generalization in energy-based models
Yilun Du and Igor Mordatch · 2019
Cited alongside, same era.
Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2019
Cited alongside, same era.
Dynamical sampling with langevin normalization flows
Minghao Gu, Shiliang Sun, and Yan Liu · 2019
Cited alongside, same era.
Neutra-lizing bad geometry in hamiltonian monte carlo using neural transport
Matthew Hoffman, Pavel Sountsov, Joshua V Dillon, Ian Langmore, Dustin Tran, and Srinivas Vasudevan · 2019
Cited alongside, same era.
Normalizing flows: Introduction and ideas
Ivan Kobyzev, Simon Prince, and Marcus A Brubaker · 2019
Cited alongside, same era.
Jiaming Song and Stefano Ermon · 2019
Later among the works it cites.
Gradient-based adaptive markov chain monte carlo
Michalis Titsias and Petros Dellaportas · 2019
Later among the works it cites.
Your gan is secretly an energy-based model and you should use discriminator driven latent sampling
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, and Yoshua Bengio · 2020
Closest in time.
Learning energy-based model with flow-based backbone by neural transport mcmc
Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, and Ying Nian Wu · 2020
Closest in time.
Modified hamiltonian monte carlo for bayesian inference
Tijana Radivojević and Elena Akhmatskaya · 2020
Closest in time.
Deep involutive generative models for neural mcmc
Span Spanbauer, Cameron Freer, and Vikash Mansinghka · 2020
Closest in time.
Metropolized flow: from invertible flow to mcmc
Achille Thin, Nikita Kotelevskii, Alain Durmus, Maxim Panov, and Eric Moulines · 2020
Closest in time.
Training deep energy-based models with f-divergence minimization
Lantao Yu, Yang Song, Jiaming Song, and Stefano Ermon · 2020
Closest in time.