2 citations · 8 across the 25 of their papers we have counts for
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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…
Feedback Guidance of Diffusion Models
Felix Koulischer, Florian Handke, Johannes Deleu +2
While Classifier-Free Guidance (CFG) has become standard for improving sample fidelity in conditional diffusion models, it can harm diversity and induce memorization by applying co…
Memorization to Generalization: Emergence of Diffusion Models from Associative Memory
Bao Pham, Gabriel Raya, Matteo Negri +3
Dense Associative Memories (DenseAMs) are generalizations of Hopfield networks, which have superior information storage capacity and can store training data points (memories) at lo…
Entropic Time Schedulers for Generative Diffusion Models
Dejan Stancevic, Florian Handke, Luca Ambrogioni
The practical performance of generative diffusion models depends on the appropriate choice of the noise scheduling function, which can also be equivalently expressed as a time repa…
The Capacity of Modern Hopfield Networks under the Data Manifold Hypothesis
Beatrice Achilli, Luca Ambrogioni, Carlo Lucibello +2
We generalize the computation of the capacity of exponential Hopfield model from Lucibello and Mézard (2024) to more generic pattern ensembles, including binary patterns and patter…
VCT: Training Consistency Models with Variational Noise Coupling
Gianluigi Silvestri, Luca Ambrogioni, Chieh-Hsin Lai +2
Consistency Training (CT) has recently emerged as a strong alternative to diffusion models for image generation. However, non-distillation CT often suffers from high variance and i…
Memorization and Generalization in Generative Diffusion under the Manifold Hypothesis
Beatrice Achilli, Luca Ambrogioni, Carlo Lucibello +2
We study the memorization and generalization capabilities of Diffusion Models (DMs) when data lies on a structured latent manifold. Specifically, we consider a set of data poin…