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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
Rectangular Flows for Manifold Learning Anthony L. Caterini Gabriel Loaiza-Ganem Geoff Pleiss John P. Cunningham DRL 111 47 0 02 Jun 2021
Latent Space Refinement for Deep Generative Models R. Winterhalder Marco Bellagente Benjamin Nachman BDL GAN DRL DiffM 126 27 0 01 Jun 2021
Fourier Space Losses for Efficient Perceptual Image Super-Resolution Dario Fuoli Luc Van Gool Radu Timofte 102 123 0 01 Jun 2021
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks David Alvarez-Melis Yair Schiff Youssef Mroueh 109 57 0 01 Jun 2021
Large-Scale Wasserstein Gradient Flows Petr Mokrov Alexander Korotin Lingxiao Li Aude Genevay Justin Solomon Evgeny Burnaev 103 76 0 01 Jun 2021
IID-GAN: an IID Sampling Perspective for Regularizing Mode Collapse Yang Li Liangliang Shi Junchi Yan GAN 40 7 0 01 Jun 2021
Transformation Models for Flexible Posteriors in Variational Bayes Sefan Hörtling Daniel Dold Oliver Durr Beate Sick 45 0 0 01 Jun 2021
Hybrid Generative Models for Two-Dimensional Datasets Hoda Shajari Jaemoon Lee Sanjay Ranka Anand Rangarajan MedIm 120 0 0 01 Jun 2021
Parallelized Computation and Backpropagation Under Angle-Parametrized Orthogonal Matrices F. Hamze 54 1 0 30 May 2021
Deconvolutional Density Network: Modeling Free-Form Conditional Distributions Bing Chen Mazharul Islam Jisuo Gao Lin Wang BDL CML 72 7 0 29 May 2021
The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider T. Aarrestad M. Beekveld M. Bona A. Boveia S. Caron ... M. White E. Wulff E. Wallin K. Wozniak Z. Zhang 95 83 0 28 May 2021
A Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market: extended version Jonathan Dumas Colin Cointe Antoine Wehenkel Antonio Sutera X. Fettweis Bertrand Cornélusse 25 9 0 28 May 2021
Augmented KRnet for density estimation and approximation Xiaoliang Wan Keju Tang 69 5 0 26 May 2021
Density estimation on low-dimensional manifolds: an inflation-deflation approach Christian Horvat J. Pfister 78 15 0 25 May 2021
Geometric variational inference Philipp Frank R. Leike T. Ensslin 76 24 0 21 May 2021
Variational Gaussian Topic Model with Invertible Neural Projections Rui Wang Deyu Zhou Yuxuan Xiong Haiping Huang BDL 66 3 0 21 May 2021
Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series Shuangshuang Chen Sihao Ding Y. Karayiannidis Mårten Björkman BDL AI4TS 47 2 0 20 May 2021
Boosting Variational Inference With Locally Adaptive Step-Sizes Gideon Dresdner Saurav Shekhar Fabian Pedregosa Francesco Locatello Gunnar Rätsch 43 2 0 19 May 2021
E(n) Equivariant Normalizing Flows Victor Garcia Satorras Emiel Hoogeboom F. Fuchs Ingmar Posner Max Welling BDL 113 182 0 19 May 2021
StackVAE-G: An efficient and interpretable model for time series anomaly detection Wenkai Li Wenbo Hu Ting Chen Ning Chen Cheng Feng BDL AI4TS 28 6 0 18 May 2021
Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement Mehmet Akccakaya Burhaneddin Yaman Hyungjin Chung Jong Chul Ye MedIm 110 56 0 17 May 2021
Mean Field Games Flock! The Reinforcement Learning Way Sarah Perrin Mathieu Laurière Julien Pérolat Matthieu Geist Romuald Élie Olivier Pietquin AI4CE 76 47 0 17 May 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 139 134 0 14 May 2021
Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech Vadim Popov Ivan Vovk Vladimir Gogoryan Tasnima Sadekova Mikhail Kudinov DiffM 117 544 0 13 May 2021
Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference Shumao Zhang Pengchuan Zhang T. Hou BDL 71 5 0 12 May 2021
NF-iSAM: Incremental Smoothing and Mapping via Normalizing Flows Qiangqiang Huang Can Pu D. Fourie Kasra Khosoussi Jonathan P. How J. Leonard 66 12 0 11 May 2021
Stochastic Image-to-Video Synthesis using cINNs Michael Dorkenwald Timo Milbich A. Blattmann Robin Rombach Konstantinos G. Derpanis Bjorn Ommer DiffM VGen 111 55 0 10 May 2021
Learning High-Dimensional Distributions with Latent Neural Fokker-Planck Kernels Yufan Zhou Changyou Chen Jinhui Xu 39 2 0 10 May 2021
Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributions Bertrand Charpentier Oliver Borchert Daniel Zügner Simon Geisler Stephan Günnemann UQCV BDL 70 17 0 10 May 2021
Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation Hojjat Navidan P. Moshiri M. Nabati Reza Shahbazian S. Ghorashi V. Shah-Mansouri David Windridge 34 87 0 10 May 2021
A likelihood approach to nonparametric estimation of a singular distribution using deep generative models Minwoo Chae Dongha Kim Yongdai Kim Lizhen Lin 97 17 0 09 May 2021
MetaKernel: Learning Variational Random Features with Limited Labels Yingjun Du Haoliang Sun Xiantong Zhen Jun Xu Yilong Yin Ling Shao Cees G. M. Snoek VLM BDL 37 5 0 08 May 2021
Interpretable machine learning for high-dimensional trajectories of aging health Spencer Farrell Arnold Mitnitski Kenneth Rockwood Andrew Rutenberg AI4CE 38 20 0 07 May 2021
COMISR: Compression-Informed Video Super-Resolution Yinxiao Li Pengchong Jin Feng Yang Ce Liu Ming-Hsuan Yang P. Milanfar SupR 65 39 0 04 May 2021
How Bayesian Should Bayesian Optimisation Be? George De Ath Richard Everson J. Fieldsend 65 6 0 03 May 2021
Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder Clément Chadebec Elina Thibeau-Sutre Ninon Burgos S. Allassonnière 129 69 0 30 Apr 2021
PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols Aaron Courville Yanpeng Zhao Kewei Tu 67 25 0 28 Apr 2021
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence David Alvarez-Melis Harmanpreet Kaur Hal Daumé Hanna M. Wallach Jennifer Wortman Vaughan FAtt 103 31 0 27 Apr 2021
System identification using Bayesian neural networks with nonparametric noise models Christos Merkatas Simo Särkkä 70 3 0 25 Apr 2021
Invertible Denoising Network: A Light Solution for Real Noise Removal Yang Liu Zhenyue Qin Saeed Anwar Pan Ji Dongwoo Kim Sabrina Caldwell Tom Gedeon 137 144 0 21 Apr 2021
Class-Incremental Learning with Generative Classifiers Gido M. van de Ven Zhe Li A. Tolias BDL 96 61 0 20 Apr 2021
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning R. Winter Frank Noé Djork-Arné Clevert BDL SSL 63 27 0 20 Apr 2021
Cycle-free CycleGAN using Invertible Generator for Unsupervised Low-Dose CT Denoising Taesung Kwon Jong Chul Ye MedIm 118 32 0 17 Apr 2021
Convolutional Normalizing Flows for Deep Gaussian Processes Haibin Yu Dapeng Liu Yizhou Chen K. H. Low Patrick Jaillet BDL 64 6 0 17 Apr 2021
Iterative Alignment Flows Zeyu Zhou Ziyu Gong Pradeep Ravikumar David I. Inouye OT DRL 93 5 0 15 Apr 2021
Learning by example: fast reliability-aware seismic imaging with normalizing flows Ali Siahkoohi Felix J. Herrmann OOD 85 13 0 13 Apr 2021
Boltzmann Tuning of Generative Models Victor Berger Michele Sebag 55 0 0 12 Apr 2021
Understanding Event-Generation Networks via Uncertainties Marco Bellagente Manuel Haussmann Michel Luchmann Tilman Plehn BDL 118 55 0 09 Apr 2021
Flow-based Spatio-Temporal Structured Prediction of Motion Dynamics Mohsen Zand Ali Etemad Michael A. Greenspan AI4CE 80 6 0 09 Apr 2021
Multilevel Stein variational gradient descent with applications to Bayesian inverse problems Terrence Alsup Luca Venturi Benjamin Peherstorfer 41 5 0 05 Apr 2021