We present a new generative model for multigraphs and weighted graphs, based on the Random Dot Product Model, that allows us to analyze these networks from a geometric perspective.

Apr 15, 2022 · We present GraphMoE, a novel neural network-based approach to learning generative models for random graphs. The neural network is trained to match the distribution …

•How to apply score-based generative modeling to discrete data? • Theoretical guidance on how to choose noise levels? • Better architecture for higher resolution image generation?

Apr 8, 2025 · Generative Adversarial Networks (GANs) provide a framework where two models (Generator and Discriminator) are trained simultaneously in a minimax game. The Generator …

Let’s consider Bayesian Classifier, which is a generative model: – A sample S of m data instances drawn iid from distribution D, (x 1;y 1);:::::;(x m;y m)˘iid D – x i 2Rd, y i 2f1;2;::::;Kg. – …

The Random Dot Product Graph (RDPG) model has emerged as a popular latent position model for the analysis and generation of undirected and unweighted network graphs.

In this paper, we abandon discretized grids and in-stead parameterize individual data points by continuous functions. We then build gener-ative models by learning distributions over such …

•A Generative Model explicitly models the actual distribution of each class •Example: Our training set is a bag of fruits. Only apples and oranges are labeled. Imagine a post-it note stuck to the …

Given samples from a data distribution q(x0), we are interested in learning a model distribution pθ(x0) that approximates q(x0) and is easy to sample from. Denoising difusion probabilistic …

Generative models learn a joint distribution over the entire dataset. They are mostly used for sampling applications or density estimation: Density estimation is estimating the probability of …