Image de couverture de livre

Paired Wasserstein Autoencoders for Conditional Sampling

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Piening, Moritz, Chung, Matthias
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Slicing Wasserstein Over Wasserstein Via Functional Optimal Transport
par: Piening, Moritz, et autres
Publié: (2025)

Slicing the Gaussian Mixture Wasserstein Distance
par: Piening, Moritz, et autres
Publié: (2025)

A Novel Sliced Fused Gromov-Wasserstein Distance
par: Piening, Moritz, et autres
Publié: (2025)

Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once
par: Piening, Moritz, et autres
Publié: (2026)

Joint Metric Space Embedding by Unbalanced OT with Gromov-Wasserstein Marginal Penalization
par: Beier, Florian, et autres
Publié: (2025)

Conditional Sampling via Wasserstein Autoencoders and Triangular Transport
par: Al-Jarrah, Mohammad, et autres
Publié: (2026)

Good Things Come in Pairs: Paired Autoencoders for Inverse Problems
par: Chung, Matthias, et autres
Publié: (2025)

Latent Space Inference via Paired Autoencoders
par: Hart, Emma, et autres
Publié: (2026)

Paired Autoencoders for Likelihood-free Estimation in Inverse Problems
par: Chung, Matthias, et autres
Publié: (2024)

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification
par: Solomon, Jack Michael, et autres
Publié: (2026)

A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization
par: Hart, Emma, et autres
Publié: (2025)

On the Information Processing of One-Dimensional Wasserstein Distances with Finite Samples
par: Jang, Cheongjae, et autres
Publié: (2025)

A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data
par: Chakraborty, Saptarshi, et autres
Publié: (2024)

Conditional Lagrangian Wasserstein Flow for Time Series Imputation
par: Qian, Weizhu, et autres
Publié: (2024)

Conditional Sig-Wasserstein GANs for Time Series Generation
par: Liao, Shujian, et autres
Publié: (2020)

S2WTM: Spherical Sliced-Wasserstein Autoencoder for Topic Modeling
par: Adhya, Suman, et autres
Publié: (2025)

Sparse $L^1$-Autoencoders for Scientific Data Compression
par: Chung, Matthias, et autres
Publié: (2024)

Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders
par: Dang, Hien, et autres
Publié: (2023)

Conditional Denoising Diffusion Autoencoders for Wireless Semantic Communications
par: Letafati, Mehdi, et autres
Publié: (2025)

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds
par: Orlichenko, Anton, et autres
Publié: (2024)

CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals
par: Garriga, Tomàs, et autres
Publié: (2026)

Autoencoding Conditional Neural Processes for Representation Learning
par: Prokhorov, Victor, et autres
Publié: (2023)

Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
par: Chemseddine, Jannis, et autres
Publié: (2024)

Aligned Training: A Parameter-Free Method to Improve Feature Quality and Stability of Sparse Autoencoders (SAE)
par: Brzozowski, Michał, et autres
Publié: (2026)

CAETC: Causal Autoencoding and Treatment Conditioning for Counterfactual Estimation over Time
par: Nguyen, Nghia D., et autres
Publié: (2026)

Conditional Diffusion Sampling
par: Castro-Macías, Francisco M., et autres
Publié: (2026)

Differentially Private Sampling from Distributions via Wasserstein Projection
par: Takakura, Shokichi, et autres
Publié: (2026)

Minimax-Optimal Two-Sample Test with Sliced Wasserstein
par: Tran, Binh Thuan, et autres
Publié: (2025)

Modeling Spatio-temporal Extremes via Conditional Variational Autoencoders
par: Ma, Xiaoyu, et autres
Publié: (2025)

Joint Hierarchical Representation Learning of Samples and Features via Informed Tree-Wasserstein Distance
par: Lin, Ya-Wei Eileen, et autres
Publié: (2025)

Coupling Generative Modeling and an Autoencoder with the Causal Bridge
par: Meng, Ruolin, et autres
Publié: (2025)

Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoders
par: Jiang, Junqi, et autres
Publié: (2025)

Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks
par: Bobbia, Benjamin, et autres
Publié: (2024)

A Unified Framework for Data-Free One-Step Sampling via Wasserstein Gradient Flows
par: Wang, Chenguang, et autres
Publié: (2026)

The Wasserstein transform
par: Jin, Kun, et autres
Publié: (2018)

Distance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning
par: Xu, Ao, et autres
Publié: (2026)

Don't Forget It! Conditional Sparse Autoencoder Clamping Works for Unlearning
par: Khoriaty, Matthew, et autres
Publié: (2025)

Fast Estimation of Wasserstein Distances via Regression on Sliced Wasserstein Distances
par: Nguyen, Khai, et autres
Publié: (2025)

A Paired Testing Protocol for Batch-Conditioned Refusal Robustness in LLM Serving
par: Kadadekar, Sahil
Publié: (2026)

Finite-Sample Wasserstein Error Bounds and Concentration Inequalities for Nonlinear Stochastic Approximation
par: Kong, Seo Taek, et autres
Publié: (2026)