50 citations · 151 across the 23 of their papers we have counts for
4 papers · 1 filter
Breaking the Likelihood-Quality Trade-off in Diffusion Models by Merging Pretrained Experts
Yasin Esfandiari, Stefan Bauer, Sebastian U. Stich +1
Diffusion models for image generation often exhibit a trade-off between perceptual sample quality and data likelihood: training objectives emphasizing high-noise denoising steps yi…
When Does Closeness in Distribution Imply Representational Similarity? An Identifiability Perspective
Beatrix M. G. Nielsen, Emanuele Marconato, Andrea Dittadi +1
When and why representations learned by different deep neural networks are similar is an active research topic. We choose to address these questions from the perspective of identif…
Minimum-Excess-Work Guidance
Christopher Kolloff, Tobias Höppe, Emmanouil Angelis +4
We propose a regularization framework inspired by thermodynamic work for guiding pre-trained probability flow generative models (e.g., continuous normalizing flows or diffusion mod…
Sparse Shift Autoencoders for Identifying Concepts from Large Language Model Activations
Shruti Joshi, Andrea Dittadi, Sébastien Lachapelle +1
Unsupervised approaches to large language model (LLM) interpretability, such as sparse autoencoders (SAEs), offer a way to decode LLM activations into interpretable and, ideally, c…