2 citations · 5 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks
Antoni Kowalczuk, Jan Dubiński, Atiyeh Ashari Ghomi +6
Large-scale vision models have become integral in many applications due to their unprecedented performance and versatility across downstream tasks. However, the robustness of these…
Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN
Mikołaj Kita, Jan Dubiński, Przemysław Rokita +1
In High Energy Physics simulations play a crucial role in unraveling the complexities of particle collision experiments within CERN's Large Hadron Collider. Machine learning simula…
Particle physics DL-simulation with control over generated data properties
Karol Rogoziński, Jan Dubiński, Przemysław Rokita +1
The research of innovative methods aimed at reducing costs and shortening the time needed for simulation, going beyond conventional approaches based on Monte Carlo methods, has bee…
Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks
Marcin Podhajski, Jan Dubiński, Franziska Boenisch +3
Graph Neural Networks (GNNs) are recognized as potent tools for processing real-world data organized in graph structures. Especially inductive GNNs, which allow for the processing…