We study generative diffusion models in the regime where both the data dimension and the
sample size are large, and the score function is trained optimally. Using statistical physics …

Understanding the structure of real data is paramount in advancing modern deep-learning
methodologies. Natural data such as images are believed to be composed of features …

Diffusion models are generative models, which gradually add and remove noise to learn the
underlying distribution of training data for data generation. The components of diffusion …

The rapid expansion of large-scale text-to-image diffusion models has raised growing
concerns regarding their potential misuse in creating harmful or misleading content. In this …

Neural network (NN) denoisers are an essential building block in many common tasks,
ranging from image reconstruction to image generation. However, the success of these …

Diffusion Models (DMs) have recently set state-of-the-art on many generation benchmarks.
However, there are myriad ways to describe them mathematically, which makes it difficult to …

Hopfield networks are widely used in neuroscience as simplified theoretical models of
biological associative memory. The original Hopfield networks store memories by encoding …

Earth Observation imagery can capture rare and unusual events such as disasters and
major landscape changes whose visual appearance contrasts with the usual observations …

Diffusion models are acclaimed for generating high-quality and diverse images. However,
their performance notably degrades when trained on data with a long-tailed distribution. For …

In this paper, we investigate the feature encoding process in a prototypical energy-based
generative model, the Restricted Boltzmann Machine (RBM). We start with an analytical …