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An Introduction to Variational Autoencoders
v1v2v3 (latest)

An Introduction to Variational Autoencoders

6 June 2019
Diederik P. Kingma
Max Welling
    BDLSSLDRL
ArXiv (abs)PDFHTML

Papers citing "An Introduction to Variational Autoencoders"

38 / 838 papers shown
Title
Data Driven Control with Learned Dynamics: Model-Based versus Model-Free
  Approach
Data Driven Control with Learned Dynamics: Model-Based versus Model-Free Approach
Wenjian Hao
Yiqiang Han
26
6
0
16 Jun 2020
Fully Unsupervised Diversity Denoising with Convolutional Variational
  Autoencoders
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
M. Prakash
Alexander Krull
Florian Jug
DiffM
47
20
0
10 Jun 2020
Learning Robust Decision Policies from Observational Data
Learning Robust Decision Policies from Observational Data
Muhammad Osama
Dave Zachariah
Petre Stoica
OODOffRL
26
5
0
03 Jun 2020
Continual Learning of Predictive Models in Video Sequences via
  Variational Autoencoders
Continual Learning of Predictive Models in Video Sequences via Variational Autoencoders
Damian Campo
Giulia Slavic
Mohamad Baydoun
L. Marcenaro
C. Regazzoni
CLLBDL
29
3
0
02 Jun 2020
Analog ensemble data assimilation and a method for constructing analogs
  with variational autoencoders
Analog ensemble data assimilation and a method for constructing analogs with variational autoencoders
Ian G. Grooms
65
29
0
01 Jun 2020
Constrained Variational Autoencoder for improving EEG based Speech
  Recognition Systems
Constrained Variational Autoencoder for improving EEG based Speech Recognition Systems
G. Krishna
Co Tran
Mason Carnahan
Ahmed H. Tewfik
DRL
15
7
0
01 Jun 2020
Introducing Latent Timbre Synthesis
Introducing Latent Timbre Synthesis
Kıvanç Tatar
D. Bisig
Philippe Pasquier
48
14
0
31 May 2020
Investigation Into the Viability of Neural Networks as a Means for
  Anomaly Detection in Experiments Like Atlas at the LHC
Investigation Into the Viability of Neural Networks as a Means for Anomaly Detection in Experiments Like Atlas at the LHC
Sully Billingsley
36
0
0
29 May 2020
Semi-supervised source localization with deep generative modeling
Semi-supervised source localization with deep generative modeling
Michael J. Bianco
Sharon Gannot
Peter Gerstoft
DRL
66
21
0
27 May 2020
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian
  Learning Analytics
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics
Zichao Wang
Yi Gu
Andrew Lan
Richard Baraniuk
57
10
0
27 May 2020
Color Visual Illusions: A Statistics-based Computational Model
Color Visual Illusions: A Statistics-based Computational Model
Elad Hirsch
A. Tal
AAML
20
0
0
18 May 2020
A Deeper Look at the Unsupervised Learning of Disentangled
  Representations in $β$-VAE from the Perspective of Core Object
  Recognition
A Deeper Look at the Unsupervised Learning of Disentangled Representations in βββ-VAE from the Perspective of Core Object Recognition
Harshvardhan Digvijay Sikka
OCLOODBDLDRL
98
1
0
25 Apr 2020
Amortized Bayesian model comparison with evidential deep learning
Amortized Bayesian model comparison with evidential deep learning
Stefan T. Radev
Marco D’Alessandro
U. Mertens
A. Voss
Ullrich Kothe
Paul-Christian Bürkner
BDL
96
34
0
22 Apr 2020
Variational Policy Propagation for Multi-agent Reinforcement Learning
Variational Policy Propagation for Multi-agent Reinforcement Learning
Chao Qu
Hui Li
Chang-rui Liu
Junwu Xiong
James Y. Zhang
Wei Chu
Weiqiang Wang
Yuan Qi
L. Song
38
0
0
19 Apr 2020
A Hybrid Objective Function for Robustness of Artificial Neural Networks
  -- Estimation of Parameters in a Mechanical System
A Hybrid Objective Function for Robustness of Artificial Neural Networks -- Estimation of Parameters in a Mechanical System
J. Sokołowski
V. Schulz
Udo Schröder
H. Beise
AAML
16
0
0
16 Apr 2020
Sequential View Synthesis with Transformer
Sequential View Synthesis with Transformer
Phong Nguyen-Ha
Lam Huynh
Esa Rahtu
J. Heikkilä
ViT
50
2
0
09 Apr 2020
State of the Art on Neural Rendering
State of the Art on Neural Rendering
A. Tewari
Ohad Fried
Justus Thies
Vincent Sitzmann
Stephen Lombardi
...
Christian Theobalt
Maneesh Agrawala
Eli Shechtman
Dan B. Goldman
Michael Zollhöfer
3DH3DV
131
473
0
08 Apr 2020
Anomaly Detection in Video Data Based on Probabilistic Latent Space
  Models
Anomaly Detection in Video Data Based on Probabilistic Latent Space Models
Giulia Slavic
Damian Campo
Mohamad Baydoun
P. Marín
David Martín
L. Marcenaro
C. Regazzoni
DRL
99
13
0
17 Mar 2020
Towards a Resilient Machine Learning Classifier -- a Case Study of
  Ransomware Detection
Towards a Resilient Machine Learning Classifier -- a Case Study of Ransomware Detection
Chih-Yuan Yang
R. Sahita
AAML
8
3
0
13 Mar 2020
Variational Inference for Deep Probabilistic Canonical Correlation
  Analysis
Variational Inference for Deep Probabilistic Canonical Correlation Analysis
Mahdi Karami
Dale Schuurmans
BDL
21
4
0
09 Mar 2020
mmFall: Fall Detection using 4D MmWave Radar and a Hybrid Variational
  RNN AutoEncoder
mmFall: Fall Detection using 4D MmWave Radar and a Hybrid Variational RNN AutoEncoder
Feng Jin
Arindam Sengupta
Siyang Cao
DRL
8
2
0
05 Mar 2020
Warwick Electron Microscopy Datasets
Warwick Electron Microscopy Datasets
Jeffrey M. Ede
105
14
0
02 Mar 2020
$π$VAE: a stochastic process prior for Bayesian deep learning with
  MCMC
πππVAE: a stochastic process prior for Bayesian deep learning with MCMC
Swapnil Mishra
Seth Flaxman
Tresnia Berah
Harrison Zhu
Mikko S. Pakkanen
Samir Bhatt
BDL
31
3
0
17 Feb 2020
Semi-supervised Grasp Detection by Representation Learning in a Vector
  Quantized Latent Space
Semi-supervised Grasp Detection by Representation Learning in a Vector Quantized Latent Space
Mridul Mahajan
Tryambak Bhattacharjee
Arya Krishnan
Priya Shukla
G. C. Nandi
DRLSSL
41
3
0
23 Jan 2020
Unsupervised Distribution Learning for Lunar Surface Anomaly Detection
Unsupervised Distribution Learning for Lunar Surface Anomaly Detection
Adam Lesnikowski
V. Bickel
Daniel Angerhausen
13
10
0
14 Jan 2020
Parameter-Conditioned Sequential Generative Modeling of Fluid Flows
Parameter-Conditioned Sequential Generative Modeling of Fluid Flows
Jeremy Morton
F. Witherden
Mykel J. Kochenderfer
GANMedImAI4CE
41
10
0
14 Dec 2019
A Closer Look at Disentangling in $β$-VAE
A Closer Look at Disentangling in βββ-VAE
Harshvardhan Digvijay Sikka
Weishun Zhong
J. Yin
Cengiz Pehlevan
CMLCoGeBDLDRL
58
15
0
11 Dec 2019
Representational Rényi heterogeneity
Representational Rényi heterogeneity
Abraham Nunes
M. Alda
T. Bardouille
Thomas Trappenberg
23
4
0
10 Dec 2019
Adversarial Robustness of Flow-Based Generative Models
Adversarial Robustness of Flow-Based Generative Models
Phillip E. Pope
Yogesh Balaji
Soheil Feizi
AAML
48
20
0
20 Nov 2019
DeVLearn: A Deep Visual Learning Framework for Localizing Temporary
  Faults in Power Systems
DeVLearn: A Deep Visual Learning Framework for Localizing Temporary Faults in Power Systems
Shuchismita Biswas
Rounak Meyur
Virgilio Centeno
26
0
0
09 Nov 2019
Toward a Better Monitoring Statistic for Profile Monitoring via
  Variational Autoencoders
Toward a Better Monitoring Statistic for Profile Monitoring via Variational Autoencoders
N. Sergin
Hao Yan
DRL
63
22
0
01 Nov 2019
Fluid Flow Mass Transport for Generative Networks
Fluid Flow Mass Transport for Generative Networks
Jingrong Lin
Keegan Lensink
Dirk Soffker
GAN
78
8
0
03 Oct 2019
Multi-task Batch Reinforcement Learning with Metric Learning
Multi-task Batch Reinforcement Learning with Metric Learning
Jiachen Li
Q. Vuong
Shuang Liu
Minghua Liu
K. Ciosek
George Andriopoulos
Henrik I. Christensen
H. Su
OffRL
65
2
0
25 Sep 2019
Prediction of rare feature combinations in population synthesis:
  Application of deep generative modelling
Prediction of rare feature combinations in population synthesis: Application of deep generative modelling
Sergio Garrido
S. Borysov
Francisco Câmara Pereira
Jeppe Rich
50
42
0
17 Sep 2019
Normalizing Flows: An Introduction and Review of Current Methods
Normalizing Flows: An Introduction and Review of Current Methods
I. Kobyzev
S. Prince
Marcus A. Brubaker
TPMMedIm
100
58
0
25 Aug 2019
DeepClean -- self-supervised artefact rejection for intensive care
  waveform data using deep generative learning
DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning
Tom Edinburgh
P. Smielewski
M. Czosnyka
S. Eglen
A. Ercole
MedIm
13
9
0
08 Aug 2019
Informed Machine Learning -- A Taxonomy and Survey of Integrating
  Knowledge into Learning Systems
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
Laura von Rueden
S. Mayer
Katharina Beckh
B. Georgiev
Sven Giesselbach
...
Rajkumar Ramamurthy
Michal Walczak
Jochen Garcke
Christian Bauckhage
Jannis Schuecker
157
653
0
29 Mar 2019
Learning the dynamics of technical trading strategies
Learning the dynamics of technical trading strategies
Nicholas Murphy
Tim Gebbie
16
5
0
06 Mar 2019
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