Normalizing Flows for Probabilistic Modeling and Inference

5 December 2019

George Papamakarios

Eric T. Nalisnick

Danilo Jimenez Rezende

S. Mohamed

Balaji Lakshminarayanan

TPM

AI4CE

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Papers citing "Normalizing Flows for Probabilistic Modeling and Inference"

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STEER: Simple Temporal Regularization For Neural ODEs Arna Ghosh Harkirat Singh Behl Emilien Dupont Philip Torr Vinay P. Namboodiri BDL AI4TS 27 74 0 18 Jun 2020
Riemannian Continuous Normalizing Flows Emile Mathieu Maximilian Nickel AI4CE 27 119 0 18 Jun 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
MoFlow: An Invertible Flow Model for Generating Molecular Graphs Chengxi Zang Fei Wang BDL 28 280 0 17 Jun 2020
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks Jens Behrmann Paul Vicol Kuan-Chieh Jackson Wang Roger C. Grosse J. Jacobsen 23 93 0 16 Jun 2020
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data Kristopher T. Jensen Ta-Chu Kao Marco Tripodi Guillaume Hennequin DRL 22 31 0 12 Jun 2020
Neural Ordinary Differential Equations on Manifolds Luca Falorsi Patrick Forré BDL AI4CE 14 33 0 11 Jun 2020
Deep Structural Causal Models for Tractable Counterfactual Inference Nick Pawlowski Daniel Coelho De Castro Ben Glocker CML MedIm 33 229 0 11 Jun 2020
Let's Face It: Probabilistic Multi-modal Interlocutor-aware Generation of Facial Gestures in Dyadic Settings Patrik Jonell Taras Kucherenko G. Henter Jonas Beskow CVBM 17 61 0 11 Jun 2020
Probabilistic Autoencoder Vanessa Böhm U. Seljak UQCV BDL DRL 21 32 0 09 Jun 2020
The Convolution Exponential and Generalized Sylvester Flows Emiel Hoogeboom Victor Garcia Satorras Jakub M. Tomczak Max Welling 25 28 0 02 Jun 2020
The role of exchangeability in causal inference O. Saarela D. Stephens E. Moodie 36 5 0 02 Jun 2020
Deep Normalization for Speaker Vectors Yunqi Cai Lantian Li Dong Wang Andrew Abel 39 25 0 07 Apr 2020
TraDE: Transformers for Density Estimation Rasool Fakoor Pratik Chaudhari Jonas W. Mueller Alex Smola 20 30 0 06 Apr 2020
Variational Inference with Vine Copulas: An efficient Approach for Bayesian Computer Model Calibration Vojtech Kejzlar T. Maiti 16 6 0 28 Mar 2020
Solving Inverse Problems with a Flow-based Noise Model Jay Whang Qi Lei A. Dimakis 64 36 0 18 Mar 2020
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models Chin-Wei Huang Laurent Dinh Aaron Courville DRL 31 87 0 17 Feb 2020
Stochastic Normalizing Flows Hao Wu Jonas Köhler Frank Noé 57 176 0 16 Feb 2020
Targeted free energy estimation via learned mappings Peter Wirnsberger A. J. Ballard George Papamakarios Stuart Abercrombie S. Racanière Alexander Pritzel Danilo Jimenez Rezende Charles Blundell 27 86 0 12 Feb 2020
On Contrastive Learning for Likelihood-free Inference Conor Durkan Iain Murray George Papamakarios BDL 45 117 0 10 Feb 2020
i-flow: High-dimensional Integration and Sampling with Normalizing Flows Christina Gao J. Isaacson Claudius Krause AI4CE 16 106 0 15 Jan 2020
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO Pablo Morales-Álvarez Pablo Ruiz S. Coughlin Rafael Molina Aggelos K. Katsaggelos 21 14 0 05 Nov 2019
The Neural Moving Average Model for Scalable Variational Inference of State Space Models Tom Ryder D. Prangle Andrew Golightly Isaac Matthews BDL AI4TS 16 6 0 02 Oct 2019
Cubic-Spline Flows Conor Durkan Artur Bekasov Iain Murray George Papamakarios TPM 48 57 0 05 Jun 2019
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference Jakob Kruse Gianluca Detommaso Ullrich Kothe Robert Scheichl 13 45 0 25 May 2019
Iterative Gaussianization: from ICA to Random Rotations Valero Laparra Gustavo Camps-Valls Jesús Malo 64 125 0 31 Jan 2016
Pixel Recurrent Neural Networks Aaron van den Oord Nal Kalchbrenner Koray Kavukcuoglu SSeg GAN 251 2,550 0 25 Jan 2016
MCMC using Hamiltonian dynamics Radford M. Neal 185 3,267 0 09 Jun 2012