ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2309.13303
  4. Cited By
C$^2$VAE: Gaussian Copula-based VAE Differing Disentangled from Coupled
  Representations with Contrastive Posterior

C2^22VAE: Gaussian Copula-based VAE Differing Disentangled from Coupled Representations with Contrastive Posterior

23 September 2023
Zhangkai Wu
LongBing Cao
    CoGe
    CML
    DRL
ArXivPDFHTML

Papers citing "C$^2$VAE: Gaussian Copula-based VAE Differing Disentangled from Coupled Representations with Contrastive Posterior"

16 / 16 papers shown
Title
Disentangling Learning Representations with Density Estimation
Disentangling Learning Representations with Density Estimation
Eric C. Yeats
Frank Liu
Hai Helen Li
BDL
DRL
CML
101
2
0
08 Feb 2023
Continual Variational Autoencoder Learning via Online Cooperative
  Memorization
Continual Variational Autoencoder Learning via Online Cooperative Memorization
Fei Ye
A. Bors
CLL
70
17
0
20 Jul 2022
Deep Generative model with Hierarchical Latent Factors for Time Series
  Anomaly Detection
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection
Cristian Challu
Peihong Jiang
Ying Nian Wu
Laurent Callot
BDL
AI4TS
41
28
0
15 Feb 2022
Multiband VAE: Latent Space Alignment for Knowledge Consolidation in
  Continual Learning
Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning
Kamil Deja
Pawel Wawrzyñski
Wojciech Masarczyk
Daniel Marczak
Tomasz Trzciñski
CLL
48
4
0
23 Jun 2021
Adversarial and Contrastive Variational Autoencoder for Sequential
  Recommendation
Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation
Zhe Xie
Chengxuan Liu
Yichi Zhang
Hongtao Lu
Dong Wang
Yue Ding
BDL
DRL
63
93
0
19 Mar 2021
Measuring Disentanglement: A Review of Metrics
Measuring Disentanglement: A Review of Metrics
M. Carbonneau
Julian Zaïdi
Jonathan Boilard
G. Gagnon
CoGe
DRL
55
84
0
16 Dec 2020
NVAE: A Deep Hierarchical Variational Autoencoder
NVAE: A Deep Hierarchical Variational Autoencoder
Arash Vahdat
Jan Kautz
BDL
69
910
0
08 Jul 2020
Progressive Learning and Disentanglement of Hierarchical Representations
Progressive Learning and Disentanglement of Hierarchical Representations
Zhiyuan Li
J. Murkute
P. Gyawali
Linwei Wang
DRL
44
40
0
24 Feb 2020
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula
  Processes
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes
David Salinas
Michael Bohlke-Schneider
Laurent Callot
Roberto Medico
Jan Gasthaus
AI4TS
61
227
0
07 Oct 2019
Modeling Tabular data using Conditional GAN
Modeling Tabular data using Conditional GAN
Lei Xu
Maria Skoularidou
Alfredo Cuesta-Infante
K. Veeramachaneni
CML
MU
SyDa
GAN
111
1,255
0
01 Jul 2019
DIVA: Domain Invariant Variational Autoencoders
DIVA: Domain Invariant Variational Autoencoders
Maximilian Ilse
Jakub M. Tomczak
Christos Louizos
Max Welling
DRL
OOD
68
202
0
24 May 2019
Contrastive Variational Autoencoder Enhances Salient Features
Contrastive Variational Autoencoder Enhances Salient Features
Abubakar Abid
James Zou
DRL
47
65
0
12 Feb 2019
Disentangling by Factorising
Disentangling by Factorising
Hyunjik Kim
A. Mnih
CoGe
OOD
62
1,348
0
16 Feb 2018
Neural Discrete Representation Learning
Neural Discrete Representation Learning
Aaron van den Oord
Oriol Vinyals
Koray Kavukcuoglu
BDL
SSL
OCL
226
5,008
0
02 Nov 2017
Importance Weighted Autoencoders
Importance Weighted Autoencoders
Yuri Burda
Roger C. Grosse
Ruslan Salakhutdinov
BDL
268
1,245
0
01 Sep 2015
Representation Learning: A Review and New Perspectives
Representation Learning: A Review and New Perspectives
Yoshua Bengio
Aaron Courville
Pascal Vincent
OOD
SSL
256
12,435
0
24 Jun 2012
1