2 citations · 8 across the 25 of their papers we have counts for
4 papers · 1 filter
Dynamic Negative Guidance of Diffusion Models
Felix Koulischer, Johannes Deleu, Gabriel Raya +2
Negative Prompting (NP) is widely utilized in diffusion models, particularly in text-to-image applications, to prevent the generation of undesired features. In this paper, we show…
Losing dimensions: Geometric memorization in generative diffusion
Beatrice Achilli, Enrico Ventura, Gianluigi Silvestri +5
Diffusion models power leading generative AI, but when and how they memorize training data, especially on low-dimensional manifolds, remains unclear. We find memorization emerges g…
Manifolds, Random Matrices and Spectral Gaps: The geometric phases of generative diffusion
Enrico Ventura, Beatrice Achilli, Gianluigi Silvestri +2
In this paper, we investigate the latent geometry of generative diffusion models under the manifold hypothesis. For this purpose, we analyze the spectrum of eigenvalues (and singul…
Robust and highly scalable estimation of directional couplings from time-shifted signals
Louis Rouillard, Luca Ambrogioni, Demian Wassermann
The estimation of directed couplings between the nodes of a network from indirect measurements is a central methodological challenge in scientific fields such as neuroscience, syst…