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Implicit Reparameterization Gradients

Implicit Reparameterization Gradients

22 May 2018
Michael Figurnov
S. Mohamed
A. Mnih
    BDL
ArXivPDFHTML

Papers citing "Implicit Reparameterization Gradients"

50 / 50 papers shown
Title
Unraveling particle dark matter with Physics-Informed Neural Networks
Unraveling particle dark matter with Physics-Informed Neural Networks
M.P. Bento
H.B. Câmara
J.F. Seabra
67
0
0
24 Feb 2025
Expert-elicitation method for non-parametric joint priors using normalizing flows
Expert-elicitation method for non-parametric joint priors using normalizing flows
F. Bockting
Stefan T. Radev
Paul-Christian Bürkner
BDL
100
1
0
24 Nov 2024
Stabilizing the Kumaraswamy Distribution
Stabilizing the Kumaraswamy Distribution
Max Wasserman
Gonzalo Mateos
BDL
47
0
0
01 Oct 2024
Analytical Approximation of the ELBO Gradient in the Context of the Clutter Problem
Analytical Approximation of the ELBO Gradient in the Context of the Clutter Problem
Roumen Nikolaev Popov
29
0
0
16 Apr 2024
Improving Variational Autoencoder Estimation from Incomplete Data with
  Mixture Variational Families
Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families
Vaidotas Šimkus
Michael U. Gutmann
48
2
0
05 Mar 2024
A VAE-based Framework for Learning Multi-Level Neural Granger-Causal
  Connectivity
A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivity
Jiahe Lin
Huitian Lei
G. Michailidis
CML
31
0
0
25 Feb 2024
Kernel Density Matrices for Probabilistic Deep Learning
Kernel Density Matrices for Probabilistic Deep Learning
Fabio A. González
Raúl Ramos-Pollán
Joseph A. Gallego-Mejia
16
2
0
26 May 2023
The Lie-Group Bayesian Learning Rule
The Lie-Group Bayesian Learning Rule
E. M. Kıral
Thomas Möllenhoff
Mohammad Emtiyaz Khan
BDL
23
2
0
08 Mar 2023
Differentiable Rendering with Reparameterized Volume Sampling
Differentiable Rendering with Reparameterized Volume Sampling
Nikita Morozov
D. Rakitin
Oleg Desheulin
Dmitry Vetrov
Kirill Struminsky
19
4
0
21 Feb 2023
RepMode: Learning to Re-parameterize Diverse Experts for Subcellular
  Structure Prediction
RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction
Donghao Zhou
Chunbin Gu
Junde Xu
Furui Liu
Qiong Wang
Guangyong Chen
Pheng-Ann Heng
MoE
18
4
0
20 Dec 2022
ADEV: Sound Automatic Differentiation of Expected Values of
  Probabilistic Programs
ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs
Alexander K. Lew
Mathieu Huot
S. Staton
Vikash K. Mansinghka
22
20
0
13 Dec 2022
SG-VAD: Stochastic Gates Based Speech Activity Detection
SG-VAD: Stochastic Gates Based Speech Activity Detection
Jonathan Svirsky
Ofir Lindenbaum
44
4
0
28 Oct 2022
Decompositional Generation Process for Instance-Dependent Partial Label
  Learning
Decompositional Generation Process for Instance-Dependent Partial Label Learning
Congyu Qiao
Ning Xu
Xin Geng
126
75
0
08 Apr 2022
Rectified Max-Value Entropy Search for Bayesian Optimization
Rectified Max-Value Entropy Search for Bayesian Optimization
Q. Nguyen
K. H. Low
Patrick Jaillet
22
5
0
28 Feb 2022
Towards Controllable Agent in MOBA Games with Generative Modeling
Towards Controllable Agent in MOBA Games with Generative Modeling
Shubao Zhang
42
0
0
15 Dec 2021
Pathologies in priors and inference for Bayesian transformers
Pathologies in priors and inference for Bayesian transformers
Tristan Cinquin
Alexander Immer
Max Horn
Vincent Fortuin
UQCV
BDL
MedIm
34
9
0
08 Oct 2021
A Hierarchical Variational Neural Uncertainty Model for Stochastic Video
  Prediction
A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction
Moitreya Chatterjee
Narendra Ahuja
A. Cherian
UQCV
VGen
BDL
42
17
0
06 Oct 2021
Stochastic Contrastive Learning
Stochastic Contrastive Learning
Jason Ramapuram
Dan Busbridge
Xavier Suau
Russ Webb
BDL
SSL
44
3
0
01 Oct 2021
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional
  Medical Image Segmentation
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation
Yingda Xia
Dong Yang
Wenqi Li
Andriy Myronenko
Daguang Xu
...
Elvira Stellato
G. Carrafiello
A. Ierardi
Alan Yuille
H. Roth
OOD
FedML
44
46
0
20 Apr 2021
Storchastic: A Framework for General Stochastic Automatic
  Differentiation
Storchastic: A Framework for General Stochastic Automatic Differentiation
Emile van Krieken
Jakub M. Tomczak
A. T. Teije
ODL
OffRL
31
15
0
01 Apr 2021
Continuous normalizing flows on manifolds
Continuous normalizing flows on manifolds
Luca Falorsi
BDL
AI4CE
30
10
0
14 Mar 2021
Self-Progressing Robust Training
Self-Progressing Robust Training
Minhao Cheng
Pin-Yu Chen
Sijia Liu
Shiyu Chang
Cho-Jui Hsieh
Payel Das
AAML
VLM
29
9
0
22 Dec 2020
Dirichlet Pruning for Neural Network Compression
Dirichlet Pruning for Neural Network Compression
Kamil Adamczewski
Mijung Park
27
3
0
10 Nov 2020
Statistical Guarantees for Transformation Based Models with Applications
  to Implicit Variational Inference
Statistical Guarantees for Transformation Based Models with Applications to Implicit Variational Inference
Sean Plummer
Shuang Zhou
A. Bhattacharya
David B. Dunson
D. Pati
DRL
16
2
0
23 Oct 2020
A Discrete Variational Recurrent Topic Model without the
  Reparametrization Trick
A Discrete Variational Recurrent Topic Model without the Reparametrization Trick
Mehdi Rezaee
Francis Ferraro
BDL
DRL
17
27
0
22 Oct 2020
not-MIWAE: Deep Generative Modelling with Missing not at Random Data
not-MIWAE: Deep Generative Modelling with Missing not at Random Data
Niels Bruun Ipsen
Pierre-Alexandre Mattei
J. Frellsen
DRL
14
54
0
23 Jun 2020
FLAMBE: Structural Complexity and Representation Learning of Low Rank
  MDPs
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
Alekh Agarwal
Sham Kakade
A. Krishnamurthy
Wen Sun
OffRL
41
223
0
18 Jun 2020
Variational Variance: Simple, Reliable, Calibrated Heteroscedastic Noise
  Variance Parameterization
Variational Variance: Simple, Reliable, Calibrated Heteroscedastic Noise Variance Parameterization
Andrew Stirn
David A. Knowles
DRL
20
10
0
08 Jun 2020
The Power Spherical distribution
The Power Spherical distribution
Nicola De Cao
Wilker Aziz
24
28
0
08 Jun 2020
Wasserstein-based Graph Alignment
Wasserstein-based Graph Alignment
Hermina Petric Maretic
Mireille El Gheche
Matthias Minder
Giovanni Chierchia
P. Frossard
OT
12
23
0
12 Mar 2020
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
Raphaël Dang-Nhu
Gagandeep Singh
Pavol Bielik
Martin Vechev
AI4TS
AAML
39
20
0
08 Mar 2020
Handling the Positive-Definite Constraint in the Bayesian Learning Rule
Handling the Positive-Definite Constraint in the Bayesian Learning Rule
Wu Lin
Mark W. Schmidt
Mohammad Emtiyaz Khan
BDL
39
35
0
24 Feb 2020
Amortised Learning by Wake-Sleep
Amortised Learning by Wake-Sleep
W. Li
Theodore H. Moskovitz
Heishiro Kanagawa
M. Sahani
OOD
23
7
0
22 Feb 2020
The Conditional Entropy Bottleneck
The Conditional Entropy Bottleneck
Ian S. Fischer
OOD
29
115
0
13 Feb 2020
A Unified Framework for Data Poisoning Attack to Graph-based
  Semi-supervised Learning
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning
Xuanqing Liu
Si Si
Xiaojin Zhu
Yang Li
Cho-Jui Hsieh
AAML
35
78
0
30 Oct 2019
Variational Auto-encoder Based Bayesian Poisson Tensor Factorization for
  Sparse and Imbalanced Count Data
Variational Auto-encoder Based Bayesian Poisson Tensor Factorization for Sparse and Imbalanced Count Data
Yuan Jin
Ming Liu
Yunfeng Li
Ruohua Xu
Lan Du
Longxiang Gao
Yong Xiang
16
5
0
12 Oct 2019
Increasing Expressivity of a Hyperspherical VAE
Increasing Expressivity of a Hyperspherical VAE
Tim R. Davidson
Jakub M. Tomczak
E. Gavves
11
6
0
07 Oct 2019
Explaining Visual Models by Causal Attribution
Explaining Visual Models by Causal Attribution
Álvaro Parafita
Jordi Vitrià
CML
FAtt
62
35
0
19 Sep 2019
Monte Carlo Gradient Estimation in Machine Learning
Monte Carlo Gradient Estimation in Machine Learning
S. Mohamed
Mihaela Rosca
Michael Figurnov
A. Mnih
45
397
0
25 Jun 2019
Scalable Bayesian dynamic covariance modeling with variational Wishart
  and inverse Wishart processes
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes
Creighton Heaukulani
Mark van der Wilk
BDL
32
15
0
22 Jun 2019
GOT: An Optimal Transport framework for Graph comparison
GOT: An Optimal Transport framework for Graph comparison
Hermina Petric Maretic
Mireille El Gheche
Giovanni Chierchia
P. Frossard
OT
25
118
0
05 Jun 2019
Nested Variational Autoencoder for Topic Modeling on Microtexts with
  Word Vectors
Nested Variational Autoencoder for Topic Modeling on Microtexts with Word Vectors
Trung Trinh
Tho Quan
Trung Mai
BDL
19
2
0
01 May 2019
Continuous Hierarchical Representations with Poincaré Variational
  Auto-Encoders
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
Emile Mathieu
Charline Le Lan
Chris J. Maddison
Ryota Tomioka
Yee Whye Teh
BDL
DRL
34
172
0
17 Jan 2019
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data
Pierre-Alexandre Mattei
J. Frellsen
SyDa
25
45
0
06 Dec 2018
Hyperprior Induced Unsupervised Disentanglement of Latent
  Representations
Hyperprior Induced Unsupervised Disentanglement of Latent Representations
Abdul Fatir Ansari
Harold Soh
CoGe
CML
UD
DRL
26
31
0
12 Sep 2018
Unbiased Implicit Variational Inference
Unbiased Implicit Variational Inference
Michalis K. Titsias
Francisco J. R. Ruiz
BDL
24
52
0
06 Aug 2018
Explorations in Homeomorphic Variational Auto-Encoding
Explorations in Homeomorphic Variational Auto-Encoding
Luca Falorsi
P. D. Haan
Tim R. Davidson
Nicola De Cao
Maurice Weiler
Patrick Forré
Taco S. Cohen
BDL
DRL
39
116
0
12 Jul 2018
Pathwise Derivatives Beyond the Reparameterization Trick
Pathwise Derivatives Beyond the Reparameterization Trick
M. Jankowiak
F. Obermeyer
30
110
0
05 Jun 2018
Stochastic Backpropagation through Mixture Density Distributions
Stochastic Backpropagation through Mixture Density Distributions
Alex Graves
BDL
57
44
0
19 Jul 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
287
9,156
0
06 Jun 2015
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