arXiv Machine Learning
Jun 4
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.
Autoencoders in Function Space
The paper introduces function‑space versions of autoencoders (FAE) and variational autoencoders (FVAE), analysing their theoretical properties and practical deployment. It highlights that the FVAE objective is well‑defined only when the data distribution aligns with the generative model, a restriction often met when data come from stochastic differential equations. In contrast, the FAE objective remains well‑defined in many cases where FVAE fails, and both can be paired with neural operator architectures to enable tasks such as inpainting, super‑resolution, and generative modelling of scientific data.
Related stories
arXiv Machine Learning
2d ago
Deep Invertible Autoencoders for Dimensionality Reduction of Dynamical Systems
arXiv:2603.13496v2 Announce Type: replace Abstract: Constructing reduced-order models (ROMs) capable of efficiently predicting the evolution of parameter-dependent high-dimensional dynamical systems...
arXiv Machine Learning
Sep 1
Partially Linear Autoencoders for Manifold Learning and Dimensionality Reduction
arXiv:2608.29867v1 Announce Type: new Abstract: Autoencoders are widely used for nonlinear dimensionality reduction and manifold learning. While most common implementations rely on both nonlinear enc...
arXiv Machine Learning
Sep 11
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.
arXiv Machine Learning
3d ago
Learning Through Energy Refinement and Manifold Projection: A Cooperative EBM-AE Framework
arXiv:2609.13917v1 Announce Type: new Abstract: Energy-Based Models (EBMs) provide a flexible framework for generative modeling by learning an energy landscape that assigns low energy values to reali...
arXiv AI
Jun 16
Improved Baselines with Representation Autoencoders
arXiv:2605. 18324v2 Announce Type: replace-cross Abstract: Representation Autoencoders (RAE) replace traditional VAE with pretrained vision encoders.
arXiv Machine Learning
Jun 25
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.
arXiv Machine Learning
Jul 1
Patch-PODiff-ViT: Structured Latent Diffusion with Patchwise POD for Super-Resolution and Uncertainty Quantification
arXiv:2606. 31290v1 Announce Type: new Abstract: Diffusion models enable probabilistic super-resolution and conditional generation, but pixel-space methods are computationally expensive and learned latent spaces often lack interpretable uncertainty quantification.
arXiv Machine Learning
Jun 9
Self-Consistent Generative Paths via Admissible Random Variational Transport
arXiv:2606. 08953v1 Announce Type: new Abstract: Modern generative models often define an entire probability path from a simple prior to the data law, rather than only an endpoint map.
arXiv AI
Jun 8
STREAM: Stochastic Riemannian Flow Matching with Anisotropic Decoder for Digital Histopathology Image Generation
arXiv:2606. 07036v1 Announce Type: cross Abstract: Synthetic histopathology image generation addresses critical challenges in computational pathology, including patient privacy and the growing need for large-scale training data for foundation models.
arXiv AI
Jul 15
Learning to Discretize: Diffusion-Based Adaptive Mesh with Spectral Guidance
arXiv:2607. 11974v1 Announce Type: cross Abstract: Most neural partial differential equation (PDE) surrogates learn how fields evolve after a grid has already been chosen.
arXiv Machine Learning
Jul 2
GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems
arXiv:2607. 01128v1 Announce Type: new Abstract: Operator learning for partial differential equations (PDEs) on arbitrary geometries builds fast neural surrogates for large-scale simulation.