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Edward: A library for probabilistic modeling, inference, and criticism
v1v2v3 (latest)

Edward: A library for probabilistic modeling, inference, and criticism

31 October 2016
Dustin Tran
A. Kucukelbir
Adji Bousso Dieng
Maja R. Rudolph
Dawen Liang
David M. Blei
ArXiv (abs)PDFHTML

Papers citing "Edward: A library for probabilistic modeling, inference, and criticism"

28 / 128 papers shown
Title
TensorFlow Distributions
TensorFlow Distributions
Joshua V. Dillon
I. Langmore
Dustin Tran
E. Brevdo
Srinivas Vasudevan
David A. Moore
Brian Patton
Alexander A. Alemi
Matt Hoffman
Rif A. Saurous
GP
229
373
0
28 Nov 2017
Advances in Variational Inference
Advances in Variational Inference
Cheng Zhang
Judith Butepage
Hedvig Kjellström
Stephan Mandt
BDL
540
770
0
15 Nov 2017
Bayesian Paragraph Vectors
Bayesian Paragraph Vectors
Geng Ji
Kushagra Pandey
Erik B. Sudderth
Stephan Mandt
BDL3DV
140
5
0
10 Nov 2017
Neural Variational Inference and Learning in Undirected Graphical Models
Neural Variational Inference and Learning in Undirected Graphical Models
Volodymyr Kuleshov
Stefano Ermon
BDL
169
34
0
07 Nov 2017
Pomegranate: fast and flexible probabilistic modeling in python
Pomegranate: fast and flexible probabilistic modeling in python
Jacob Schreiber
171
178
0
31 Oct 2017
Implicit Causal Models for Genome-wide Association Studies
Implicit Causal Models for Genome-wide Association StudiesInternational Conference on Learning Representations (ICLR), 2017
Dustin Tran
David M. Blei
CML
93
45
0
30 Oct 2017
Bayesian Inference over the Stiefel Manifold via the Givens
  Representation
Bayesian Inference over the Stiefel Manifold via the Givens RepresentationBayesian Analysis (BA), 2017
A. Pourzanjani
Richard M. Jiang
Brian Mitchell
P. Atzberger
Linda R. Petzold
253
9
0
25 Oct 2017
sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo
sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo
Jack Baker
Paul Fearnhead
E. Fox
Christopher Nemeth
BDL
211
11
0
02 Oct 2017
ZhuSuan: A Library for Bayesian Deep Learning
ZhuSuan: A Library for Bayesian Deep Learning
Jiaxin Shi
Jianfei Chen
Jun Zhu
Shengyang Sun
Yucen Luo
Yihong Gu
Yuhao Zhou
UQCVBDL
135
43
0
18 Sep 2017
Forecasting of commercial sales with large scale Gaussian Processes
Forecasting of commercial sales with large scale Gaussian Processes
Rodrigo Rivera
Evgeny Burnaev
133
21
0
16 Sep 2017
ELFI: Engine for Likelihood-Free Inference
ELFI: Engine for Likelihood-Free Inference
Jarno Lintusaari
H. Vuollekoski
A. Kangasrääsiö
Kusti Skytén
Marko Jarvenpaa
Pekka Marttinen
Michael U. Gutmann
Aki Vehtari
J. Corander
Samuel Kaski
218
79
0
02 Aug 2017
Control Variates for Stochastic Gradient MCMC
Control Variates for Stochastic Gradient MCMC
Jack Baker
Paul Fearnhead
E. Fox
Christopher Nemeth
BDL
237
105
0
16 Jun 2017
Deep Learning: A Bayesian Perspective
Deep Learning: A Bayesian Perspective
Nicholas G. Polson
Vadim Sokolov
BDL
357
126
0
01 Jun 2017
Neural Models for Documents with Metadata
Neural Models for Documents with Metadata
Dallas Card
Chenhao Tan
Noah A. Smith
BDL
239
128
0
25 May 2017
Proximity Variational Inference
Proximity Variational Inference
Jaan Altosaar
Rajesh Ranganath
David M. Blei
BDL
115
22
0
24 May 2017
Causal Effect Inference with Deep Latent-Variable Models
Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos
Uri Shalit
Joris Mooij
David Sontag
R. Zemel
Max Welling
CMLBDL
393
825
0
24 May 2017
Learning Probabilistic Programs Using Backpropagation
Learning Probabilistic Programs Using Backpropagation
Avi Pfeffer
BDL
98
0
0
15 May 2017
Importance Sampled Stochastic Optimization for Variational Inference
Importance Sampled Stochastic Optimization for Variational Inference
J. Sakaya
Arto Klami
BDL
155
8
0
19 Apr 2017
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for
  Variational Inference
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference
Geoffrey Roeder
Yuhuai Wu
David Duvenaud
BDL
409
212
0
27 Mar 2017
Dynamic Bernoulli Embeddings for Language Evolution
Dynamic Bernoulli Embeddings for Language Evolution
Maja R. Rudolph
David M. Blei
BDL
146
35
0
23 Mar 2017
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Dustin Tran
Rajesh Ranganath
David M. Blei
VLMGAN
267
100
0
28 Feb 2017
Deep Probabilistic Programming
Deep Probabilistic ProgrammingInternational Conference on Learning Representations (ICLR), 2017
Dustin Tran
Matthew D. Hoffman
Rif A. Saurous
E. Brevdo
Kevin Patrick Murphy
David M. Blei
BDL
244
200
0
13 Jan 2017
Variational Inference via $χ$-Upper Bound Minimization
Variational Inference via χχχ-Upper Bound Minimization
Adji Bousso Dieng
Dustin Tran
Rajesh Ranganath
John Paisley
David M. Blei
BDL
477
35
0
01 Nov 2016
Automatic Differentiation Variational Inference
Automatic Differentiation Variational Inference
A. Kucukelbir
Dustin Tran
Rajesh Ranganath
Andrew Gelman
David M. Blei
260
763
0
02 Mar 2016
Hierarchical Variational Models
Hierarchical Variational Models
Rajesh Ranganath
Dustin Tran
David M. Blei
DRLVLM
252
350
0
07 Nov 2015
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
Stephen H. Bach
Matthias Broecheler
Bert Huang
Lise Getoor
TPMAI4CE
340
409
0
17 May 2015
Automatic differentiation in machine learning: a survey
Automatic differentiation in machine learning: a survey
A. G. Baydin
Barak A. Pearlmutter
Alexey Radul
J. Siskind
PINNAI4CEODL
502
3,235
0
20 Feb 2015
Indian Buffet Process Deep Generative Models for Semi-Supervised
  Classification
Indian Buffet Process Deep Generative Models for Semi-Supervised Classification
S. Chatzis
330
4
0
14 Feb 2014
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