2 citations · 5 across the 14 of their papers we have counts for
14 papers
Dataset Usage Inference without Shadow Models or Held-out Data
Wojciech Łapacz, Stanisław Pawlak, Jan Dubiński +2
How much of my data was used to train a machine learning model? Dataset Usage Inference (DUI) aims to answer this by estimating what fraction of a dataset contributed to a model's…
On Stealing Graph Neural Network Models
Marcin Podhajski, Jan Dubiński, Franziska Boenisch +3
Current graph neural network (GNN) model-stealing methods rely heavily on queries to the victim model, assuming no hard query limits. However, in reality, the number of allowed que…
Backdoor Vectors: a Task Arithmetic View on Backdoor Attacks and Defenses
Stanisław Pawlak, Jan Dubiński, Daniel Marczak +1
Model merging (MM) recently emerged as an effective method for combining large deep learning models. However, it poses significant security risks. Recent research shows that it is…
Universal Properties of Activation Sparsity in Modern Large Language Models
Filip Szatkowski, Patryk Będkowski, Alessio Devoto +5
Activation sparsity is an intriguing property of deep neural networks that has been extensively studied in ReLU-based models, due to its advantages for efficiency, robustness, and…
Privacy Attacks on Image AutoRegressive Models
Antoni Kowalczuk, Jan Dubiński, Franziska Boenisch +1
Image AutoRegressive generation has emerged as a new powerful paradigm with image autoregressive models (IARs) matching state-of-the-art diffusion models (DMs) in image quality (FI…
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…
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders
Jan Dubiński, Stanisław Pawlak, Franziska Boenisch +2
Machine Learning as a Service (MLaaS) APIs provide ready-to-use and high-utility encoders that generate vector representations for given inputs. Since these encoders are very costl…
Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN
Jan Dubiński, Kamil Deja, Sandro Wenzel +2
Currently, over half of the computing power at CERN GRID is used to run High Energy Physics simulations. The recent updates at the Large Hadron Collider (LHC) create the need for d…
Progressive Latent Replay for efficient Generative Rehearsal
Stanisław Pawlak, Filip Szatkowski, Michał Bortkiewicz +2
We introduce a new method for internal replay that modulates the frequency of rehearsal based on the depth of the network. While replay strategies mitigate the effects of catastrop…
Selectively increasing the diversity of GAN-generated samples
Jan Dubiński, Kamil Deja, Sandro Wenzel +2
Generative Adversarial Networks (GANs) are powerful models able to synthesize data samples closely resembling the distribution of real data, yet the diversity of those generated sa…