2 citations · 8 across the 25 of their papers we have counts for
10 papers · 1 filter
The Entropic Signature of Class Speciation in Diffusion Models
Florian Handke, Dejan Stančević, Felix Koulischer +2
Diffusion models do not recover semantic structure uniformly over time. Instead, samples transition from semantic ambiguity to class commitment within a narrow regime. Recent theor…
Emergence of Distortions in High-Dimensional Guided Diffusion Models
Enrico Ventura, Beatrice Achilli, Luca Ambrogioni +1
Classifier-free guidance (CFG) is the de facto standard for conditional sampling in diffusion models, yet it often reduces sample diversity. Using tools from statistical physics, w…
The Information Dynamics of Generative Diffusion
Dejan Stancevic, Luca Ambrogioni
Generative diffusion models have emerged as a powerful class of models in machine learning, yet a unified theoretical understanding of their operation is still developing. This pap…
CoVAE: Consistency Training of Variational Autoencoders
Gianluigi Silvestri, Luca Ambrogioni
Current state-of-the-art generative approaches frequently rely on a two-stage training procedure, where an autoencoder (often a VAE) first performs dimensionality reduction, follow…
Measuring Semantic Information Production in Generative Diffusion Models
Florian Handke, Félix Koulischer, Gabriel Raya +1
It is well known that semantic and structural features of the generated images emerge at different times during the reverse dynamics of diffusion, a phenomenon that has been connec…
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…
The statistical thermodynamics of generative diffusion models: Phase transitions, symmetry breaking and critical instability
Luca Ambrogioni
Generative diffusion models have achieved spectacular performance in many areas of machine learning and generative modeling. While the fundamental ideas behind these models come fr…
Stationarity without mean reversion in improper Gaussian processes
Luca Ambrogioni
The behavior of a GP regression depends on the choice of covariance function. Stationary covariance functions are preferred in machine learning applications. However, (non-periodic…
In search of dispersed memories: Generative diffusion models are associative memory networks
Luca Ambrogioni
Uncovering the mechanisms behind long-term memory is one of the most fascinating open problems in neuroscience and artificial intelligence. Artificial associative memory networks h…