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Neural Spline Flows

Neural Spline Flows

10 June 2019
Conor Durkan
Artur Bekasov
Iain Murray
George Papamakarios
    DRL
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Papers citing "Neural Spline Flows"

34 / 184 papers shown
Title
Sequential Neural Posterior and Likelihood Approximation
Sequential Neural Posterior and Likelihood Approximation
Samuel Wiqvist
J. Frellsen
Umberto Picchini
BDL
34
33
0
12 Feb 2021
Benchmarking Simulation-Based Inference
Benchmarking Simulation-Based Inference
Jan-Matthis Lueckmann
Jan Boelts
David S. Greenberg
P. J. Gonçalves
Jakob H. Macke
104
186
0
12 Jan 2021
Variational Determinant Estimation with Spherical Normalizing Flows
Variational Determinant Estimation with Spherical Normalizing Flows
Simon Passenheim
Emiel Hoogeboom
BDL
31
1
0
24 Dec 2020
On The Verification of Neural ODEs with Stochastic Guarantees
On The Verification of Neural ODEs with Stochastic Guarantees
Sophie Gruenbacher
Ramin Hasani
Mathias Lechner
J. Cyranka
S. Smolka
Radu Grosu
77
31
0
16 Dec 2020
Convex Potential Flows: Universal Probability Distributions with Optimal
  Transport and Convex Optimization
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
Chin-Wei Huang
Ricky T. Q. Chen
Christos Tsirigotis
Aaron Courville
OT
119
95
0
10 Dec 2020
Self Normalizing Flows
Self Normalizing Flows
Thomas Anderson Keller
Jorn W. T. Peters
P. Jaini
Emiel Hoogeboom
Patrick Forré
Max Welling
30
14
0
14 Nov 2020
Causal Autoregressive Flows
Causal Autoregressive Flows
Ilyes Khemakhem
R. Monti
R. Leech
Aapo Hyvarinen
CML
OOD
AI4CE
21
108
0
04 Nov 2020
Generative Temporal Difference Learning for Infinite-Horizon Prediction
Generative Temporal Difference Learning for Infinite-Horizon Prediction
Michael Janner
Igor Mordatch
Sergey Levine
AI4CE
18
34
0
27 Oct 2020
Neural Approximate Sufficient Statistics for Implicit Models
Neural Approximate Sufficient Statistics for Implicit Models
Yanzhi Chen
Dinghuai Zhang
Michael U. Gutmann
Aaron Courville
Zhanxing Zhu
30
79
0
20 Oct 2020
Improving Sequential Latent Variable Models with Autoregressive Flows
Improving Sequential Latent Variable Models with Autoregressive Flows
Joseph Marino
Lei Chen
Jiawei He
Stephan Mandt
BDL
AI4TS
30
12
0
07 Oct 2020
Variational Inference with Continuously-Indexed Normalizing Flows
Variational Inference with Continuously-Indexed Normalizing Flows
Anthony L. Caterini
R. Cornish
Dino Sejdinovic
Arnaud Doucet
BDL
24
19
0
10 Jul 2020
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
Andreas Lugmayr
Martin Danelljan
Luc Van Gool
Radu Timofte
SupR
DRL
23
356
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
27
60
0
22 Jun 2020
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph
  modularity
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
S. Udrescu
A. Tan
Jiahai Feng
Orisvaldo Neto
Tailin Wu
Max Tegmark
19
185
0
18 Jun 2020
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and
  Optimization
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization
Abhinav Agrawal
Daniel Sheldon
Justin Domke
TPM
BDL
13
38
0
18 Jun 2020
Categorical Normalizing Flows via Continuous Transformations
Categorical Normalizing Flows via Continuous Transformations
Phillip Lippe
E. Gavves
BDL
21
43
0
17 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
23
93
0
16 Jun 2020
Ordering Dimensions with Nested Dropout Normalizing Flows
Ordering Dimensions with Nested Dropout Normalizing Flows
Artur Bekasov
Iain Murray
DRL
28
5
0
15 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
33
229
0
11 Jun 2020
Probabilistic Autoencoder
Probabilistic Autoencoder
Vanessa Böhm
U. Seljak
UQCV
BDL
DRL
24
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
30
28
0
02 Jun 2020
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment
  Search
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search
Jaehyeon Kim
Sungwon Kim
Jungil Kong
Sungroh Yoon
54
477
0
22 May 2020
TraDE: Transformers for Density Estimation
TraDE: Transformers for Density Estimation
Rasool Fakoor
Pratik Chaudhari
Jonas W. Mueller
Alex Smola
20
30
0
06 Apr 2020
Composing Normalizing Flows for Inverse Problems
Composing Normalizing Flows for Inverse Problems
Jay Whang
Erik M. Lindgren
A. Dimakis
TPM
26
50
0
26 Feb 2020
Stochastic Normalizing Flows
Stochastic Normalizing Flows
Hao Wu
Jonas Köhler
Frank Noé
57
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
27
86
0
12 Feb 2020
i-flow: High-dimensional Integration and Sampling with Normalizing Flows
i-flow: High-dimensional Integration and Sampling with Normalizing Flows
Christina Gao
J. Isaacson
Claudius Krause
AI4CE
24
107
0
15 Jan 2020
Normalizing Flows for Probabilistic Modeling and Inference
Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios
Eric T. Nalisnick
Danilo Jimenez Rezende
S. Mohamed
Balaji Lakshminarayanan
TPM
AI4CE
62
1,631
0
05 Dec 2019
FIS-GAN: GAN with Flow-based Importance Sampling
FIS-GAN: GAN with Flow-based Importance Sampling
Shiyu Yi
Donglin Zhan
Wenqing Zhang
Zhengyang Geng
Kang An
Hao Wang
GAN
27
3
0
06 Oct 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
16
6
0
02 Oct 2019
Cubic-Spline Flows
Cubic-Spline Flows
Conor Durkan
Artur Bekasov
Iain Murray
George Papamakarios
TPM
53
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
13
45
0
25 May 2019
A RAD approach to deep mixture models
A RAD approach to deep mixture models
Laurent Dinh
Jascha Narain Sohl-Dickstein
Hugo Larochelle
Razvan Pascanu
22
45
0
18 Mar 2019
Pixel Recurrent Neural Networks
Pixel Recurrent Neural Networks
Aaron van den Oord
Nal Kalchbrenner
Koray Kavukcuoglu
SSeg
GAN
272
2,553
0
25 Jan 2016
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