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Variational Inference with Normalizing Flows

21 May 2015

Danilo Jimenez Rezende

Papers citing "Variational Inference with Normalizing Flows"

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Title
Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks N. Jeffrey Benjamin Dan Wandelt 75 39 0 11 Nov 2020
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference M. Vandegar Michael Kagan Antoine Wehenkel Gilles Louppe 91 28 0 11 Nov 2020
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE Ding Zhou Xue-Xin Wei DRL 262 84 0 09 Nov 2020
Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis Ron J. Weiss RJ Skerry-Ryan Eric Battenberg Soroosh Mariooryad Diederik P. Kingma 99 101 0 06 Nov 2020
Causal Autoregressive Flows Ilyes Khemakhem R. Monti R. Leech Aapo Hyvarinen CML OOD AI4CE 107 111 0 04 Nov 2020
Problems using deep generative models for probabilistic audio source separation M. Frank Maximilian Ilse DiffM 74 4 0 03 Nov 2020
Amortized Variational Deep Q Network Haotian Zhang Yuhao Wang Jianyong Sun Zongben Xu BDL 46 0 0 03 Nov 2020
Learning on Attribute-Missing Graphs Xu Chen Siheng Chen Jiangchao Yao Huangjie Zheng Ya Zhang Ivor W Tsang 93 94 0 03 Nov 2020
Transforming Gaussian Processes With Normalizing Flows Juan Maroñas Oliver Hamelijnck Jeremias Knoblauch Theodoros Damoulas 105 34 0 03 Nov 2020
Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models Yuchen Wu Melissa Mozifian Florian Shkurti 90 21 0 02 Nov 2020
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective Vu-Linh Nguyen Vaden Masrani Rob Brekelmans Michael A. Osborne Frank Wood 52 5 0 29 Oct 2020
Generative Temporal Difference Learning for Infinite-Horizon Prediction Michael Janner Igor Mordatch Sergey Levine AI4CE 79 35 0 27 Oct 2020
Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging He Sun Katherine Bouman UQCV 63 75 0 27 Oct 2020
Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks Thomas Bird F. Kingma David Barber SyDa MQ AI4CE 143 9 0 26 Oct 2020
Scalable Gaussian Process Variational Autoencoders Metod Jazbec Matthew Ashman Vincent Fortuin Michael Pearce Stephan Mandt Gunnar Rätsch DRL BDL 89 29 0 26 Oct 2020
ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows Julen Urain M. Ginesi Davide Tateo Jan Peters AI4CE 86 58 0 25 Oct 2020
Variational Bayesian Unlearning Q. Nguyen Bryan Kian Hsiang Low Patrick Jaillet BDL MU 98 128 0 24 Oct 2020
Accelerating Reinforcement Learning with Learned Skill Priors Karl Pertsch Youngwoon Lee Joseph J. Lim OffRL OnRL 114 241 0 22 Oct 2020
Measure Transport with Kernel Stein Discrepancy Matthew A. Fisher T. Nolan Matthew M. Graham D. Prangle Chris J. Oates OT 124 15 0 22 Oct 2020
Geometry-Aware Hamiltonian Variational Auto-Encoder Clément Chadebec Clément Mantoux S. Allassonnière DRL 84 16 0 22 Oct 2020
NU-GAN: High resolution neural upsampling with GAN Rithesh Kumar Kundan Kumar Vicki Anand Yoshua Bengio Aaron Courville 65 26 0 22 Oct 2020
Principled Interpolation in Normalizing Flows Samuel G. Fadel Sebastian Mair Ricardo da S. Torres Ulf Brefeld 146 3 0 22 Oct 2020
Learning to Plan Optimally with Flow-based Motion Planner Tin Lai F. Ramos 72 7 0 21 Oct 2020
Semi-parametric $γ$ -ray modeling with Gaussian processes and variational inference S. Mishra-Sharma Kyle Cranmer MedIm 102 7 0 20 Oct 2020
Probabilistic Character Motion Synthesis using a Hierarchical Deep Latent Variable Model Saeed Ghorbani C. Wloka Ali Etemad M. Brubaker N. Troje 3DV 78 32 0 20 Oct 2020
Hierarchical Autoregressive Modeling for Neural Video Compression Ruihan Yang Yibo Yang Joseph Marino Stephan Mandt BDL VGen 183 47 0 19 Oct 2020
Orbital MCMC Kirill Neklyudov Max Welling 66 7 0 15 Oct 2020
Deep Generative Modeling in Network Science with Applications to Public Policy Research Gavin Hartnett R. Vardavas Lawrence Baker Michael Chaykowski C. Gibson F. Girosi David Kennedy Osonde A. Osoba 26 2 0 15 Oct 2020
Deep Conditional Transformation Models Philipp F. M. Baumann Torsten Hothorn David Rügamer 46 29 0 15 Oct 2020
Training Invertible Linear Layers through Rank-One Perturbations Andreas Krämer Jonas Köhler Frank Noé 49 0 0 14 Oct 2020
Simulation-based inference methods for particle physics Johann Brehmer Kyle Cranmer AI4CE 126 22 0 13 Oct 2020
Real-time parameter inference in reduced-order flame models with heteroscedastic Bayesian neural network ensembles Ushnish Sengupta Maximilian L. Croci M. Juniper 20 3 0 11 Oct 2020
Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis Yicheng Luo A. Filieri Yuanshuo Zhou 55 5 0 10 Oct 2020
No MCMC for me: Amortized sampling for fast and stable training of energy-based models Will Grathwohl Jacob Kelly Milad Hashemi Mohammad Norouzi Kevin Swersky David Duvenaud 120 72 0 08 Oct 2020
Information Theory Measures via Multidimensional Gaussianization Valero Laparra J. E. Johnson Gustau Camps-Valls Raúl Santos-Rodríguez Jesús Malo 70 11 0 08 Oct 2020
Representing Point Clouds with Generative Conditional Invertible Flow Networks Michal Stypulkowski Kacper Kania M. Zamorski Maciej Ziȩba Tomasz Trzciñski J. Chorowski 3DPC 97 4 0 07 Oct 2020
Automatic Backward Filtering Forward Guiding for Markov processes and graphical models Frank van der Meulen Moritz Schauer 88 12 0 07 Oct 2020
Learning from demonstration using products of experts: applications to manipulation and task prioritization Emmanuel Pignat João Silvério Sylvain Calinon 46 18 0 07 Oct 2020
Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders Benoit Gaujac Ilya Feige David Barber DiffM BDL 62 0 0 07 Oct 2020
Improving Sequential Latent Variable Models with Autoregressive Flows Joseph Marino Lei Chen Jiawei He Stephan Mandt BDL AI4TS 127 12 0 07 Oct 2020
Denoising Diffusion Implicit Models Jiaming Song Chenlin Meng Stefano Ermon VLM DiffM 334 7,531 0 06 Oct 2020
Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains Francisco J. R. Ruiz Michalis K. Titsias taylan. cemgil Arnaud Doucet BDL DRL 65 14 0 05 Oct 2020
Assisting the Adversary to Improve GAN Training Andreas Munk William Harvey Frank Wood GAN 35 0 0 03 Oct 2020
MCMC-Interactive Variational Inference Quan Zhang Huangjie Zheng Mingyuan Zhou 56 1 0 02 Oct 2020
Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers Hanjie Chen Yangfeng Ji AAML VLM 107 66 0 01 Oct 2020
VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models Zhisheng Xiao Karsten Kreis Jan Kautz Arash Vahdat 116 124 0 01 Oct 2020
Geometric Disentanglement by Random Convex Polytopes M. Joswig M. Kaluba Lukas Ruff 65 3 0 29 Sep 2020
Secure Data Sharing With Flow Model Chenwei Wu Chenzhuang Du Yang Yuan FedML 22 4 0 24 Sep 2020
Probabilistic Machine Learning for Healthcare Irene Y. Chen Shalmali Joshi Marzyeh Ghassemi Rajesh Ranganath OOD 74 56 0 23 Sep 2020
On the representation and learning of monotone triangular transport maps Ricardo Baptista Youssef Marzouk O. Zahm 103 49 0 22 Sep 2020