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Normalizing Flows for Probabilistic Modeling and Inference

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"

27 / 277 papers shown
Title
Conditional Set Generation with Transformers
Conditional Set Generation with Transformers
Adam R. Kosiorek
Hyunjik Kim
Danilo Jimenez Rezende
19
40
0
26 Jun 2020
Relative gradient optimization of the Jacobian term in unsupervised deep
  learning
Relative gradient optimization of the Jacobian term in unsupervised deep learning
Luigi Gresele
G. Fissore
Adrián Javaloy
Bernhard Schölkopf
Aapo Hyvarinen
DRL
6
22
0
26 Jun 2020
The Gaussian equivalence of generative models for learning with shallow
  neural networks
The Gaussian equivalence of generative models for learning with shallow neural networks
Sebastian Goldt
Bruno Loureiro
Galen Reeves
Florent Krzakala
M. Mézard
Lenka Zdeborová
BDL
33
100
0
25 Jun 2020
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless
  Compression
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression
Rianne van den Berg
A. Gritsenko
Mostafa Dehghani
C. Sønderby
Tim Salimans
17
59
0
22 Jun 2020
STEER: Simple Temporal Regularization For Neural ODEs
STEER: Simple Temporal Regularization For Neural ODEs
Arna Ghosh
Harkirat Singh Behl
Emilien Dupont
Philip H. S. Torr
Vinay P. Namboodiri
BDL
AI4TS
19
74
0
18 Jun 2020
Riemannian Continuous Normalizing Flows
Riemannian Continuous Normalizing Flows
Emile Mathieu
Maximilian Nickel
AI4CE
25
119
0
18 Jun 2020
Understanding and Mitigating Exploding Inverses in Invertible Neural
  Networks
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks
Jens Behrmann
Paul Vicol
Kuan-Chieh Jackson Wang
Roger C. Grosse
J. Jacobsen
8
92
0
16 Jun 2020
Manifold GPLVMs for discovering non-Euclidean latent structure in neural
  data
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
Kristopher T. Jensen
Ta-Chu Kao
Marco Tripodi
Guillaume Hennequin
DRL
13
31
0
12 Jun 2020
Neural Ordinary Differential Equations on Manifolds
Neural Ordinary Differential Equations on Manifolds
Luca Falorsi
Patrick Forré
BDL
AI4CE
6
33
0
11 Jun 2020
Deep Structural Causal Models for Tractable Counterfactual Inference
Deep Structural Causal Models for Tractable Counterfactual Inference
Nick Pawlowski
Daniel Coelho De Castro
Ben Glocker
CML
MedIm
25
229
0
11 Jun 2020
Let's Face It: Probabilistic Multi-modal Interlocutor-aware Generation
  of Facial Gestures in Dyadic Settings
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
15
60
0
11 Jun 2020
Probabilistic Autoencoder
Probabilistic Autoencoder
Vanessa Böhm
U. Seljak
UQCV
BDL
DRL
16
32
0
09 Jun 2020
The Convolution Exponential and Generalized Sylvester Flows
The Convolution Exponential and Generalized Sylvester Flows
Emiel Hoogeboom
Victor Garcia Satorras
Jakub M. Tomczak
Max Welling
17
28
0
02 Jun 2020
The role of exchangeability in causal inference
The role of exchangeability in causal inference
O. Saarela
D. Stephens
E. Moodie
27
5
0
02 Jun 2020
Deep Normalization for Speaker Vectors
Deep Normalization for Speaker Vectors
Yunqi Cai
Lantian Li
Dong Wang
Andrew Abel
21
25
0
07 Apr 2020
Solving Inverse Problems with a Flow-based Noise Model
Solving Inverse Problems with a Flow-based Noise Model
Jay Whang
Qi Lei
A. Dimakis
56
36
0
18 Mar 2020
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows
  and Latent Variable Models
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
Chin-Wei Huang
Laurent Dinh
Aaron Courville
DRL
24
87
0
17 Feb 2020
Stochastic Normalizing Flows
Stochastic Normalizing Flows
Hao Wu
Jonas Köhler
Frank Noé
37
176
0
16 Feb 2020
Targeted free energy estimation via learned mappings
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
11
86
0
12 Feb 2020
On Contrastive Learning for Likelihood-free Inference
On Contrastive Learning for Likelihood-free Inference
Conor Durkan
Iain Murray
George Papamakarios
BDL
34
117
0
10 Feb 2020
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch
  Detection in LIGO
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO
Pablo Morales-Álvarez
Pablo Ruiz
S. Coughlin
Rafael Molina
Aggelos K. Katsaggelos
16
14
0
05 Nov 2019
The Neural Moving Average Model for Scalable Variational Inference of
  State Space Models
The Neural Moving Average Model for Scalable Variational Inference of State Space Models
Tom Ryder
D. Prangle
Andrew Golightly
Isaac Matthews
BDL
AI4TS
14
6
0
02 Oct 2019
Cubic-Spline Flows
Cubic-Spline Flows
Conor Durkan
Artur Bekasov
Iain Murray
George Papamakarios
TPM
40
57
0
05 Jun 2019
HINT: Hierarchical Invertible Neural Transport for Density Estimation
  and Bayesian Inference
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference
Jakob Kruse
Gianluca Detommaso
Ullrich Kothe
Robert Scheichl
11
45
0
25 May 2019
Iterative Gaussianization: from ICA to Random Rotations
Iterative Gaussianization: from ICA to Random Rotations
Valero Laparra
Gustavo Camps-Valls
Jesús Malo
64
125
0
31 Jan 2016
Pixel Recurrent Neural Networks
Pixel Recurrent Neural Networks
Aaron van den Oord
Nal Kalchbrenner
Koray Kavukcuoglu
SSeg
GAN
230
2,545
0
25 Jan 2016
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
182
3,262
0
09 Jun 2012
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