Variational Autoencoder Layer

Variational Autoencoder Layer

arXiv:2606. 25900v1 Announce Type: new Abstract: Variational Autoencoders (VAEs) belong to a family of autoencoders with probabilistic properties, making them well suited for generating data by producing a smooth and continuous latent space.

Variational lossy autoencoder

Geometry-Preserving Encoder/Decoder in Latent Generative Models

arXiv:2501. 09876v3 Announce Type: replace-cross Abstract: Generative modeling aims to generate new data samples that resemble a given dataset.

Variational Augmented Invertible Koopman Autoencoder for probabilistic time series forecasting

arXiv:2609.37435v1 Announce Type: new Abstract: Neural Koopman autoencoder models have been shown to successfully build a latent embedding with linear dynamics for arbitrary dynamical systems, enabli...

Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics

arXiv:2604. 00669v2 Announce Type: replace Abstract: This study examines the challenges of modeling complex and noisy data related to socioeconomic factors over time, with a focus on data from various districts in Odisha, India.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis

arXiv:2506. 00849v2 Announce Type: replace Abstract: Despite the empirical success of Diffusion Models (DMs) and Variational Autoencoders (VAEs), their generalization performance remains theoretically underexplored, especially lacking a full consideration of the shared encoder-generator structure.