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2002.05227
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Variational Autoencoders with Riemannian Brownian Motion Priors
International Conference on Machine Learning (ICML), 2020
12 February 2020
Dimitris Kalatzis
David Eklund
Georgios Arvanitidis
Søren Hauberg
BDL
DRL
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Papers citing
"Variational Autoencoders with Riemannian Brownian Motion Priors"
35 / 35 papers shown
PepCompass: Navigating peptide embedding spaces using Riemannian Geometry
Marcin Mo.zejko
Adam Bielecki
Jurand Prądzyński
Marcin Traskowski
Antoni Janowski
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Marcelo Der Torossian Torres
Cesar de la Fuente-Nunez
Paulina Szymczak
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Ewa Szczurek
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02 Oct 2025
Privacy-Utility Trade-off in Data Publication: A Bilevel Optimization Framework with Curvature-Guided Perturbation
Yi Yin
G. Zhang
Hua Zuo
Jie Lu
239
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0
02 Sep 2025
Assessing local deformation and computing scalar curvature with nonlinear conformal regularization of decoders
Benjamin Couéraud
V. Sunkara
Christof Schütte
182
0
0
28 Aug 2025
Riemannian generative decoder
Andreas Bjerregaard
Søren Hauberg
Anders Krogh
DRL
DiffM
390
4
0
23 Jun 2025
Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models
Louis Bethune
David Vigouroux
Yilun Du
Rufin VanRullen
Thomas Serre
Victor Boutin
DiffM
647
3
0
23 May 2025
Counterfactual Explanations via Riemannian Latent Space Traversal
Paraskevas Pegios
Aasa Feragen
Andreas Abildtrup Hansen
Georgios Arvanitidis
BDL
317
8
0
04 Nov 2024
Decoder ensembling for learned latent geometries
Stas Syrota
Pablo Moreno-Muñoz
Søren Hauberg
DRL
AI4CE
310
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0
14 Aug 2024
Variational autoencoder with weighted samples for high-dimensional non-parametric adaptive importance sampling
J. Demange-Chryst
François Bachoc
Jérome Morio
Timothé Krauth
341
4
0
13 Oct 2023
Manifold-augmented Eikonal Equations: Geodesic Distances and Flows on Differentiable Manifolds
Daniel Kelshaw
Luca Magri
224
0
0
09 Oct 2023
VTAE: Variational Transformer Autoencoder with Manifolds Learning
IEEE Transactions on Image Processing (IEEE TIP), 2023
Pourya Shamsolmoali
Masoumeh Zareapoor
Huiyu Zhou
Dacheng Tao
Xuelong Li
DRL
276
16
0
03 Apr 2023
Variational Inference for Longitudinal Data Using Normalizing Flows
Clément Chadebec
S. Allassonnière
BDL
DRL
214
1
0
24 Mar 2023
Quadratic Matrix Factorization with Applications to Manifold Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
Zheng Zhai
Hengchao Chen
Qiang Sun
254
5
0
30 Jan 2023
A Geometric Perspective on Variational Autoencoders
Neural Information Processing Systems (NeurIPS), 2022
Clément Chadebec
S. Allassonnière
DRL
334
35
0
15 Sep 2022
Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case
Neural Information Processing Systems (NeurIPS), 2022
Clément Chadebec
Louis J. Vincent
S. Allassonnière
DRL
254
39
0
16 Jun 2022
GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions
Journal of Computational Physics (JCP), 2022
Ryan Lopez
P. Atzberger
AI4CE
467
12
0
10 Jun 2022
Visualizing Riemannian data with Rie-SNE
A. Bergsson
Søren Hauberg
205
5
0
17 Mar 2022
Reactive Motion Generation on Learned Riemannian Manifolds
Hadi Beik-Mohammadi
Søren Hauberg
Georgios Arvanitidis
Gerhard Neumann
Leonel Rozo
343
19
0
15 Mar 2022
Preventing Manifold Intrusion with Locality: Local Mixup
Raphael Baena
Lucas Drumetz
Vincent Gripon
AAML
366
17
0
12 Jan 2022
EditVAE: Unsupervised Part-Aware Controllable 3D Point Cloud Shape Generation
Shidi Li
Miaomiao Liu
Christian J. Walder
3DPC
282
35
0
13 Oct 2021
On the Latent Holes of VAEs for Text Generation
Ruizhe Li
Xutan Peng
Chenghua Lin
256
5
0
07 Oct 2021
Exploring the Latent Space of Autoencoders with Interventional Assays
Neural Information Processing Systems (NeurIPS), 2021
Felix Leeb
Stefan Bauer
M. Besserve
Bernhard Schölkopf
DRL
395
23
0
30 Jun 2021
On the Generative Utility of Cyclic Conditionals
Neural Information Processing Systems (NeurIPS), 2021
Yu Xie
Haoyue Tang
Tao Qin
Jintao Wang
Tie-Yan Liu
269
4
0
30 Jun 2021
Learning Identity-Preserving Transformations on Data Manifolds
Marissa Connor
Kion Fallah
Christopher Rozell
284
5
0
22 Jun 2021
Learning Riemannian Manifolds for Geodesic Motion Skills
Hadi Beik-Mohammadi
Søren Hauberg
Georgios Arvanitidis
Gerhard Neumann
Leonel Rozo
252
37
0
08 Jun 2021
Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
Clément Chadebec
Elina Thibeau-Sutre
Ninon Burgos
S. Allassonnière
454
92
0
30 Apr 2021
Boltzmann Tuning of Generative Models
Victor Berger
Michele Sebag
178
0
0
12 Apr 2021
Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning
Neural Information Processing Systems (NeurIPS), 2021
Guy Tennenholtz
Shie Mannor
OffRL
263
16
0
22 Feb 2021
Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder
Computer Vision and Pattern Recognition (CVPR), 2020
Tal Daniel
Aviv Tamar
DRL
423
50
0
24 Dec 2020
Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems
Ryan Lopez
P. Atzberger
DRL
AI4CE
290
11
0
07 Dec 2020
Generative Capacity of Probabilistic Protein Sequence Models
Nature Communications (Nat Commun), 2020
Francisco McGee
Quentin Novinger
R. Levy
Vincenzo Carnevale
A. Haldane
331
41
0
03 Dec 2020
What is a meaningful representation of protein sequences?
Nature Communications (Nat Commun), 2020
N. Detlefsen
Søren Hauberg
Wouter Boomsma
565
142
0
28 Nov 2020
Learning Manifold Implicitly via Explicit Heat-Kernel Learning
Neural Information Processing Systems (NeurIPS), 2020
Jiuxiang Gu
Changyou Chen
Jinhui Xu
323
9
0
05 Oct 2020
Manifolds for Unsupervised Visual Anomaly Detection
Louise Naud
Alexander Lavin
DRL
245
7
0
19 Jun 2020
Variational Autoencoder with Learned Latent Structure
Marissa Connor
Gregory H. Canal
Christopher Rozell
CML
DRL
406
58
0
18 Jun 2020
A Metric Space for Point Process Excitations
Journal of Artificial Intelligence Research (JAIR), 2020
Myrl G. Marmarelis
Greg Ver Steeg
Aram Galstyan
379
1
0
05 May 2020
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