Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference

27 March 2017

Papers citing "Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference"

50 / 52 papers shown

Title
Path Gradients after Flow Matching Lorenz Vaitl Leon Klein 14 0 0 15 May 2025
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold Hoang Phuc Hau Luu Hanlin Yu Bernardo Williams Marcelo Hartmann Arto Klami DRL 41 0 0 03 Oct 2024
SoftCVI: Contrastive variational inference with self-generated soft labels Daniel Ward Mark Beaumont Matteo Fasiolo BDL 53 0 0 22 Jul 2024
Particle Semi-Implicit Variational Inference Jen Ning Lim A. M. Johansen 51 4 0 30 Jun 2024
Dream-in-Style: Text-to-3D Generation Using Stylized Score Distillation Hubert Kompanowski Binh-Son Hua DiffM 64 3 0 05 Jun 2024
Scalable Bayesian Learning with posteriors Samuel Duffield Kaelan Donatella Johnathan Chiu Phoebe Klett Daniel Simpson BDL UQCV 62 1 0 31 May 2024
Fast and Unified Path Gradient Estimators for Normalizing Flows Lorenz Vaitl Ludwig Winkler Lorenz Richter Pan Kessel 39 4 0 23 Mar 2024
Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders Yaniv Yacoby Weiwei Pan Finale Doshi-Velez DRL 29 0 0 13 Mar 2024
Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families Vaidotas Šimkus Michael U. Gutmann 43 2 0 05 Mar 2024
Stable Training of Normalizing Flows for High-dimensional Variational Inference Daniel Andrade BDL TPM 43 1 0 26 Feb 2024
Stochastic Optimal Control Matching Carles Domingo-Enrich Jiequn Han Brandon Amos Joan Bruna Ricky T. Q. Chen DiffM 20 6 0 04 Dec 2023
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization Dinghuai Zhang Ricky Tian Qi Chen Cheng-Hao Liu Aaron C. Courville Yoshua Bengio 34 41 0 04 Oct 2023
Variational Prediction Alexander A. Alemi Ben Poole BDL 16 2 0 14 Jul 2023
Function-Space Regularization for Deep Bayesian Classification J. Lin Joe Watson Pascal Klink Jan Peters UQCV BDL 41 1 0 12 Jul 2023
Improved sampling via learned diffusions Lorenz Richter Julius Berner DiffM 37 52 0 03 Jul 2023
Provable convergence guarantees for black-box variational inference Justin Domke Guillaume Garrigos Robert Mansel Gower 18 18 0 04 Jun 2023
VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems Yingzhi Xia Qifeng Liao Jinglai Li 27 2 0 22 Feb 2023
An optimal control perspective on diffusion-based generative modeling Julius Berner Lorenz Richter Karen Ullrich DiffM 39 80 0 02 Nov 2022
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images Matthew D. Hoffman T. Le Pavel Sountsov Christopher Suter Ben Lee Vikash K. Mansinghka Rif A. Saurous BDL 29 12 0 27 Oct 2022
DreamFusion: Text-to-3D using 2D Diffusion Ben Poole Ajay Jain Jonathan T. Barron B. Mildenhall 76 2,319 0 29 Sep 2022
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models Oleg Arenz Philipp Dahlinger Zihan Ye Michael Volpp Gerhard Neumann 39 15 0 23 Sep 2022
Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows Lorenz Vaitl K. Nicoli Shinichi Nakajima Pan Kessel 61 24 0 17 Jul 2022
Latent Variable Modelling Using Variational Autoencoders: A survey Vasanth Kalingeri CML DRL 26 2 0 20 Jun 2022
Path-Gradient Estimators for Continuous Normalizing Flows Lorenz Vaitl K. Nicoli Shinichi Nakajima Pan Kessel 27 13 0 17 Jun 2022
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients Kyurae Kim Jisu Oh Jacob R. Gardner Adji Bousso Dieng Hongseok Kim BDL 32 8 0 13 Jun 2022
Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization Adrián Javaloy Maryam Meghdadi Isabel Valera 27 27 0 09 Jun 2022
Variational Sparse Coding with Learned Thresholding Kion Fallah Christopher Rozell DRL 25 7 0 07 May 2022
Variational methods for simulation-based inference Manuel Glöckler Michael Deistler Jakob H. Macke 30 46 0 08 Mar 2022
Bayesian Nonparametrics for Offline Skill Discovery Valentin Villecroze H. Braviner Panteha Naderian Chris J. Maddison G. Loaiza-Ganem BDL OffRL 26 8 0 09 Feb 2022
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery Chris Cundy Aditya Grover Stefano Ermon CML 40 72 0 06 Dec 2021
Challenging the Semi-Supervised VAE Framework for Text Classification G. Felhi Joseph Le Roux Djamé Seddah BDL 21 2 0 27 Sep 2021
Nested Variational Inference Heiko Zimmermann Hao Wu Babak Esmaeili Jan-Willem van de Meent BDL 29 20 0 21 Jun 2021
Empirical Evaluation of Biased Methods for Alpha Divergence Minimization Tomas Geffner Justin Domke 30 9 0 13 May 2021
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations Winnie Xu Ricky T. Q. Chen Xuechen Li David Duvenaud BDL UQCV 27 46 0 12 Feb 2021
VarGrad: A Low-Variance Gradient Estimator for Variational Inference Lorenz Richter Ayman Boustati Nikolas Nusken Francisco J. R. Ruiz Ömer Deniz Akyildiz DRL 138 48 0 20 Oct 2020
Flexible mean field variational inference using mixtures of non-overlapping exponential families J. Spence 17 4 0 14 Oct 2020
Controlling the Interaction Between Generation and Inference in Semi-Supervised Variational Autoencoders Using Importance Weighting G. Felhi Joseph Leroux Djamé Seddah BDL 21 1 0 13 Oct 2020
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks Yaniv Yacoby Weiwei Pan Finale Doshi-Velez CML DRL 27 25 0 14 Jul 2020
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization Abhinav Agrawal Daniel Sheldon Justin Domke TPM BDL 8 38 0 18 Jun 2020
Mutual Information Gradient Estimation for Representation Learning Liangjiang Wen Yiji Zhou Lirong He Mingyuan Zhou Zenglin Xu DRL SSL 33 27 0 03 May 2020
Estimating Gradients for Discrete Random Variables by Sampling without Replacement W. Kool H. V. Hoof Max Welling BDL 31 49 0 14 Feb 2020
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax Andres Potapczynski G. Loaiza-Ganem John P. Cunningham 32 29 0 19 Dec 2019
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models Yuge Shi Siddharth Narayanaswamy Brooks Paige Philip Torr DRL 27 266 0 08 Nov 2019
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Jan Stühmer Richard Turner Sebastian Nowozin DRL BDL CoGe 117 25 0 05 Sep 2019
PixelVAE++: Improved PixelVAE with Discrete Prior Hossein Sadeghi Evgeny Andriyash W. Vinci L. Buffoni Mohammad H. Amin BDL DRL 21 33 0 26 Aug 2019
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Pierre-Alexandre Mattei J. Frellsen SyDa 25 45 0 06 Dec 2018
A Review of Learning with Deep Generative Models from Perspective of Graphical Modeling Zhijian Ou 31 16 0 05 Aug 2018
Quasi-Monte Carlo Variational Inference Alexander K. Buchholz F. Wenzel Stephan Mandt BDL 25 58 0 04 Jul 2018
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation Manuel Haussmann Fred Hamprecht M. Kandemir BDL 26 6 0 19 May 2018
Gaussian variational approximation for high-dimensional state space models M. Quiroz David J. Nott Robert Kohn 24 40 0 24 Jan 2018