On the quantitative analysis of decoder-based generative models

The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer

arXiv:2605.29713v2 Announce Type: replace-cross Abstract: This book provides a compact, derivation-oriented introduction to the mathematical foundations of modern generative artificial intelligence....

Transformer-based Encoder-Decoder Models

FFJORD: Free-form continuous dynamics for scalable reversible generative models

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.

Generative Models: Principles, Architectures, and Applications

arXiv:2608. 08101v1 Announce Type: new Abstract: Generative AI has emerged as one of the most transformative forces in modern artificial intelligence, reshaping how we create, imagine, and interact with digital content.

Bridging Reconstruction and Generation: A Latent Distribution Perspective on Evaluation and Improvement

The paper investigates why reconstruction quality in latent generative models does not always predict generative performance, attributing the issue to a mismatch between encoder-induced and generation-time latent distributions. It introduces Generation‑Aware Reconstruction (GAR), a method that perturbs encoder latents with noise and denoises them through the generative model before decoding, creating a continuous trajectory that reveals how the decoder behaves across latent spaces. The resulting GAR‑FID metric correlates strongly with generation FID, and using intermediate GAR latents for decoder adaptation consistently improves generative quality across different model scales.

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.

Building Expressive and Tractable Probabilistic Generative Models: A Review

The article surveys recent progress in tractable probabilistic generative modeling, with a focus on Probabilistic Circuits (PCs). It offers a unified view of the trade‑offs between expressivity and tractability, outlining design principles, algorithmic extensions, and a taxonomy of the field. The review also covers deep and hybrid PCs that integrate ideas from deep neural models, and highlights challenges and open questions for future research.

How to generate text: using different decoding methods for language generation with Transformers

An invertible generative model for forward and inverse problems

arXiv:2509. 03910v2 Announce Type: replace-cross Abstract: We formulate inverse problems in a Bayesian framework and aim to train an invertible generative model that is capable of simulation (i.

Introduction to Stochastic Differential Equations for Generative Machine Learning: A Variational Perspective

arXiv:2606. 31576v1 Announce Type: new Abstract: The use of ordinary and stochastic differential equations has led to substantial progress in generative machine learning with applications to, for example, image, video and biomolecule generation.

Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability

arXiv:2606. 21385v2 Announce Type: replace-cross Abstract: This paper explores unsupervised disentangled representation learning from a functional perspective.