Improved off-policy training of diffusion samplers

7 February 2024

Nikolay Malkin

Papers citing "Improved off-policy training of diffusion samplers"

33 / 83 papers shown

Title
Path Integral Sampler: a stochastic control approach for sampling Qinsheng Zhang Yongxin Chen DiffM 85 114 0 30 Nov 2021
GFlowNet Foundations Yoshua Bengio Salem Lahlou T. Deleu J. E. Hu Mo Tiwari Emmanuel Bengio 48 230 0 17 Nov 2021
Amortized Variational Inference for Simple Hierarchical Models Abhinav Agrawal Justin Domke BDL 34 24 0 04 Nov 2021
Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory T. Chen Guan-Horng Liu Evangelos A. Theodorou DiffM OT 212 174 0 21 Oct 2021
Diffusion Normalizing Flow Qinsheng Zhang Yongxin Chen DiffM 63 92 0 14 Oct 2021
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation Emmanuel Bengio Moksh Jain Maksym Korablyov Doina Precup Yoshua Bengio 101 328 0 08 Jun 2021
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 72 385 0 29 Apr 2021
Improved Denoising Diffusion Probabilistic Models Alex Nichol Prafulla Dhariwal DiffM 337 3,686 0 18 Feb 2021
Nested Sampling Methods J. Buchner 65 61 0 24 Jan 2021
Maximum Likelihood Training of Score-Based Diffusion Models Yang Song Conor Durkan Iain Murray Stefano Ermon DiffM 148 665 0 22 Jan 2021
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 335 6,480 0 26 Nov 2020
VarGrad: A Low-Variance Gradient Estimator for Variational Inference Lorenz Richter Ayman Boustati Nikolas Nusken Francisco J. R. Ruiz Ömer Deniz Akyildiz DRL 188 51 0 20 Oct 2020
Denoising Diffusion Probabilistic Models Jonathan Ho Ajay Jain Pieter Abbeel DiffM 625 18,096 0 19 Jun 2020
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining Austin Tripp Erik A. Daxberger José Miguel Hernández-Lobato MedIm 63 140 0 16 Jun 2020
Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space Nikolas Nusken Lorenz Richter AI4CE 64 111 0 11 May 2020
BayesFlow: Learning complex stochastic models with invertible neural networks Stefan T. Radev U. Mertens A. Voss Lynton Ardizzone Ullrich Kothe BDL 288 197 0 13 Mar 2020
Stochastic Normalizing Flows Hao Wu Jonas Köhler Frank Noé 127 185 0 16 Feb 2020
i-flow: High-dimensional Integration and Sampling with Normalizing Flows Christina Gao J. Isaacson Claudius Krause AI4CE 48 109 0 15 Jan 2020
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit Belinda Tzen Maxim Raginsky DiffM 164 210 0 23 May 2019
Flow-based generative models for Markov chain Monte Carlo in lattice field theory M. S. Albergo G. Kanwar P. Shanahan AI4CE 51 218 0 26 Apr 2019
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport Matthew Hoffman Pavel Sountsov Joshua V. Dillon I. Langmore Dustin Tran Srinivas Vasudevan BDL 58 106 0 09 Mar 2019
Theoretical guarantees for sampling and inference in generative models with latent diffusions Belinda Tzen Maxim Raginsky DiffM 64 101 0 05 Mar 2019
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models Will Grathwohl Ricky T. Q. Chen J. Bettencourt Ilya Sutskever David Duvenaud DRL 144 873 0 02 Oct 2018
Faster Eigenvector Computation via Shift-and-Invert Preconditioning Dan Garber Laurent Dinh Chi Jin Jascha Narain Sohl-Dickstein Samy Bengio Praneeth Netrapalli Aaron Sidford 266 3,696 0 26 May 2016
Importance Weighted Autoencoders Yuri Burda Roger C. Grosse Ruslan Salakhutdinov BDL 268 1,245 0 01 Sep 2015
Variational Inference with Normalizing Flows Danilo Jimenez Rezende S. Mohamed DRL BDL 313 4,182 0 21 May 2015
Deep Unsupervised Learning using Nonequilibrium Thermodynamics Jascha Narain Sohl-Dickstein Eric A. Weiss Niru Maheswaranathan Surya Ganguli SyDa DiffM 301 6,949 0 12 Mar 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks José Miguel Hernández-Lobato Ryan P. Adams UQCV BDL 127 945 0 18 Feb 2015
Black Box Variational Inference Rajesh Ranganath S. Gerrish David M. Blei DRL BDL 136 1,167 0 31 Dec 2013
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 452 16,933 0 20 Dec 2013
Stochastic Variational Inference Matt Hoffman David M. Blei Chong-Jun Wang John Paisley BDL 259 2,622 0 29 Jun 2012
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman Andrew Gelman 165 4,302 0 18 Nov 2011
Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions Natesh S. Pillai Andrew M. Stuart Alexandre Hoang Thiery 90 99 0 02 Mar 2011