On the quantitative analysis of decoder-based generative models

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

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.

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.

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.

Notes on generative modeling: flow matching, diffusion, optimal transport and Schr{ö}dinger bridge

These notes recapitulate the high level mathematical principles behind different techniques for generative modeling. I show the connections between optimal transport and standard techniques such as Schr{ö}dinger bridge and flow matching.

Generative models

This post describes four projects that share a common theme of enhancing or using generative models, a branch of unsupervised learning techniques in machine learning. In addition to describing our work, this post will tell you a bit more about generative models: what they are, why they are important, and where they might be going.

Concentration bounds on response-based vector embeddings of black-box generative models

arXiv:2511. 08307v2 Announce Type: replace-cross Abstract: Generative models, such as large language models or text-to-image diffusion models, can generate relevant responses to user-given queries.

T5Gemma: A new collection of encoder-decoder Gemma models

Introducing T5Gemma, a new collection of encoder-decoder LLMs.

Markov Chain Decoders Overcome the Heavy-Tail Limitations of Lipschitz Generative Models

arXiv:2605. 18931v2 Announce Type: replace-cross Abstract: Heavy-tailed distributions are prevalent in performance evaluation, network traffic, and risk modeling.

Discovering and decoding latent mean-field structure with variational autoencoders

arXiv:2606. 08694v1 Announce Type: cross Abstract: Generative models are increasingly used to capture correlations in many-body systems, but the representations they learn remain largely opaque to physical interpretation.