Normalizing Flows are generative models which produce tractable distributions where both
sampling and density evaluation can be efficient and exact. The goal of this survey article is …

Recently, hyperbolic deep neural networks (HDNNs) have been gaining momentum as the
deep representations in the hyperbolic space provide high fidelity embeddings with few …

Score-based generative models (SGMs) are a powerful class of generative models that
exhibit remarkable empirical performance. Score-based generative modelling (SGM) …

Diffusion models are recent state-of-the-art methods for image generation and likelihood
estimation. In this work, we generalize continuous-time diffusion models to arbitrary …

We propose Riemannian Flow Matching (RFM), a simple yet powerful framework for training
continuous normalizing flows on manifolds. Existing methods for generative modeling on …

We introduce manifold-learning flows (ℳ-flows), a new class of generative models that
simultaneously learn the data manifold as well as a tractable probability density on that …

Hyperbolic neural networks have shown great potential for modeling complex data.
However, existing hyperbolic networks are not completely hyperbolic, as they encode …

Normalizing flows have shown great promise for modelling flexible probability distributions
in a computationally tractable way. However, whilst data is often naturally described on …

Normalizing flows are a promising tool for modeling probability distributions in physical
systems. While state-of-the-art flows accurately approximate distributions and energies …

Graph neural networks generalize conventional neural networks to graph-structured data
and have received widespread attention due to their impressive representation ability. In …