activity
20232026
most citedDynamic Negative Guidance of Diffusion Models

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

stat.ML2026

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…

stat.ML2025

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…

cs.CV2025

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…

cs.CV2024★ 2 cited

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…

cs.LG2023

Exploring the Temperature-Dependent Phase Transition in Modern Hopfield Networks

Felix Koulischer, Cédric Goemaere, Tom van der Meersch +2

The recent discovery of a connection between Transformers and Modern Hopfield Networks (MHNs) has reignited the study of neural networks from a physical energy-based perspective. T…