1 citations · 1 across the 2 of their papers we have counts for
4 papers
ExpertSim: Fast Particle Detector Simulation Using Mixture-of-Generative-Experts
Patryk Będkowski, Jan Dubiński, Filip Szatkowski +3
Simulating detector responses is a crucial part of understanding the inner workings of particle collisions in the Large Hadron Collider at CERN. Such simulations are currently perf…
SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders
Bartosz Cywiński, Kamil Deja
Diffusion models, while powerful, can inadvertently generate harmful or undesirable content, raising significant ethical and safety concerns. Recent machine unlearning approaches o…
Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation
Joanna Kaleta, Paweł Skierś, Jan Dubiński +2
We introduce Mediffusion -- a new method for semi-supervised learning with explainable classification based on a joint diffusion model. The medical imaging domain faces unique chal…
Joint Diffusion models in Continual Learning
Paweł Skierś, Kamil Deja
In this work, we introduce JDCL - a new method for continual learning with generative rehearsal based on joint diffusion models. Neural networks suffer from catastrophic forgetting…