NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport

9 March 2019

Matthew Hoffman

Papers citing "NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport"

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Title
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models Sam Bond-Taylor Adam Leach Yang Long Chris G. Willcocks VLM TPM 36 478 0 08 Mar 2021
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC P. Jaini Didrik Nielsen Max Welling BDL 35 10 0 04 Feb 2021
Orbital MCMC Kirill Neklyudov Max Welling 15 7 0 15 Oct 2020
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting Adam D. Cobb Brian Jalaian BDL 14 73 0 14 Oct 2020
A Neural Network MCMC sampler that maximizes Proposal Entropy Zengyi Li Yubei Chen Friedrich T. Sommer 25 14 0 07 Oct 2020
VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models Zhisheng Xiao Karsten Kreis Jan Kautz Arash Vahdat 11 123 0 01 Oct 2020
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems Aman Sinha Matthew O'Kelly Russ Tedrake John C. Duchi 39 47 0 24 Aug 2020
Involutive MCMC: a Unifying Framework Kirill Neklyudov Max Welling Evgenii Egorov Dmitry Vetrov 8 36 0 30 Jun 2020
Telescoping Density-Ratio Estimation Benjamin Rhodes Kai Xu Michael U. Gutmann 17 94 0 22 Jun 2020
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC Erik Nijkamp Ruiqi Gao Pavel Sountsov Srinivas Vasudevan Bo Pang Song-Chun Zhu Ying Nian Wu BDL 8 20 0 12 Jun 2020
Bayesian Neural Networks Tom Charnock Laurence Perreault Levasseur F. Lanusse UQCV BDL 13 3 0 02 Jun 2020
Markovian Score Climbing: Variational Inference with KL(p\|\|q) C. A. Naesseth Fredrik Lindsten David M. Blei 115 54 0 23 Mar 2020
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling Tong Che Ruixiang Zhang Jascha Narain Sohl-Dickstein Hugo Larochelle Liam Paull Yuan Cao Yoshua Bengio DiffM DRL 12 111 0 12 Mar 2020
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference Achille Thin Nikita Kotelevskii Jean-Stanislas Denain Léo Grinsztajn Alain Durmus Maxim Panov Eric Moulines BDL 6 17 0 27 Feb 2020
Composing Normalizing Flows for Inverse Problems Jay Whang Erik M. Lindgren A. Dimakis TPM 26 49 0 26 Feb 2020
Stochastic Normalizing Flows Hao Wu Jonas Köhler Frank Noé 39 176 0 16 Feb 2020
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware Junpeng Lao Christopher Suter I. Langmore C. Chimisov A. Saxena Pavel Sountsov Dave Moore Rif A. Saurous Matthew D. Hoffman Joshua V. Dillon 9 30 0 04 Feb 2020
i-flow: High-dimensional Integration and Sampling with Normalizing Flows Christina Gao J. Isaacson Claudius Krause AI4CE 11 105 0 15 Jan 2020
Hamiltonian Monte Carlo Swindles Dan Piponi Matthew D. Hoffman Pavel Sountsov 8 9 0 14 Jan 2020
Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios Eric T. Nalisnick Danilo Jimenez Rezende S. Mohamed Balaji Lakshminarayanan TPM AI4CE 31 1,623 0 05 Dec 2019
Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs Víctor Gallego D. Insua BDL 8 1 0 26 Aug 2019
Learning Symmetries of Classical Integrable Systems Roberto Bondesan A. Lamacraft 14 39 0 11 Jun 2019
Automatic Reparameterisation of Probabilistic Programs Maria I. Gorinova Dave Moore Matthew D. Hoffman 17 28 0 07 Jun 2019
A Condition Number for Hamiltonian Monte Carlo I. Langmore M. Dikovsky S. Geraedts Peter C. Norgaard R. V. Behren 18 6 0 23 May 2019
Importance Sampling-based Transport Map Hamiltonian Monte Carlo for Bayesian Hierarchical Models Kjartan Kloster Osmundsen T. S. Kleppe R. Liesenfeld 19 3 0 19 Dec 2018