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Universal Marginaliser for Deep Amortised Inference for Probabilistic
  Programs

Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs

16 October 2019
R. Walecki
Kostis Gourgoulias
Adam Baker
Chris Hart
Chris Lucas
Max Zwiessele
A. Buchard
Maria Lomeli
Yura N. Perov
Saurabh Johri
    UQCV
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Papers citing "Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs"

16 / 16 papers shown
Title
Learning about an exponential amount of conditional distributions
Learning about an exponential amount of conditional distributions
Mohamed Ishmael Belghazi
Maxime Oquab
Yann LeCun
David Lopez-Paz
BDL
SSL
36
28
0
22 Feb 2019
Universal Marginalizer for Amortised Inference and Embedding of
  Generative Models
Universal Marginalizer for Amortised Inference and Embedding of Generative Models
R. Walecki
A. Buchard
Kostis Gourgoulias
Chris Hart
Maria Lomeli
Alexandre Khae Wu Navarro
Max Zwiessele
Yura N. Perov
Saurabh Johri
BDL
55
2
0
12 Nov 2018
Pyro: Deep Universal Probabilistic Programming
Pyro: Deep Universal Probabilistic Programming
Eli Bingham
Jonathan P. Chen
M. Jankowiak
F. Obermeyer
Neeraj Pradhan
Theofanis Karaletsos
Rohit Singh
Paul A. Szerlip
Paul Horsfall
Noah D. Goodman
BDL
GP
115
1,043
0
18 Oct 2018
Variational Autoencoder with Arbitrary Conditioning
Variational Autoencoder with Arbitrary Conditioning
Oleg Ivanov
Michael Figurnov
Dmitry Vetrov
BDL
DRL
42
146
0
06 Jun 2018
A Universal Marginalizer for Amortized Inference in Generative Models
A Universal Marginalizer for Amortized Inference in Generative Models
Laura Douglas
Iliyan Zarov
Kostis Gourgoulias
Chris Lucas
Chris Hart
Adam Baker
M. Sahani
Yura N. Perov
Saurabh Johri
UQCV
CML
41
31
0
02 Nov 2017
Attention Is All You Need
Attention Is All You Need
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan Gomez
Lukasz Kaiser
Illia Polosukhin
3DV
526
129,831
0
12 Jun 2017
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning
T. Le
A. G. Baydin
R. Zinkov
Frank Wood
SyDa
OOD
104
89
0
02 Mar 2017
Inference Compilation and Universal Probabilistic Programming
Inference Compilation and Universal Probabilistic Programming
T. Le
A. G. Baydin
Frank Wood
UQCV
159
143
0
31 Oct 2016
Deep Amortized Inference for Probabilistic Programs
Deep Amortized Inference for Probabilistic Programs
Daniel E. Ritchie
Paul Horsfall
Noah D. Goodman
TPM
85
82
0
18 Oct 2016
Inference Networks for Sequential Monte Carlo in Graphical Models
Inference Networks for Sequential Monte Carlo in Graphical Models
Brooks Paige
Frank Wood
BDL
122
110
0
22 Feb 2016
Data-driven Sequential Monte Carlo in Probabilistic Programming
Data-driven Sequential Monte Carlo in Probabilistic Programming
Yura N. Perov
T. Le
Frank Wood
BDL
20
7
0
14 Dec 2015
Neural Adaptive Sequential Monte Carlo
Neural Adaptive Sequential Monte Carlo
S. Gu
Zoubin Ghahramani
Richard Turner
BDL
55
147
0
10 Jun 2015
MADE: Masked Autoencoder for Distribution Estimation
MADE: Masked Autoencoder for Distribution Estimation
M. Germain
Karol Gregor
Iain Murray
Hugo Larochelle
OOD
SyDa
UQCV
132
863
0
12 Feb 2015
An Empirical Analysis of Likelihood-Weighting Simulation on a Large,
  Multiply-Connected Belief Network
An Empirical Analysis of Likelihood-Weighting Simulation on a Large, Multiply-Connected Belief Network
M. Shwe
G. Cooper
93
89
0
27 Mar 2013
Recognition Networks for Approximate Inference in BN20 Networks
Recognition Networks for Approximate Inference in BN20 Networks
Q. Morris
BDL
121
27
0
10 Jan 2013
Conformant Planning via Symbolic Model Checking
Conformant Planning via Symbolic Model Checking
A. Cimatti
M. Roveri
73
961
0
01 Jun 2011
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