1 citations · 1 across the 8 of their papers we have counts for
8 papers
Fast LapSum: Exact Differentiable Top- at Million Scale
Łukasz Struski, Joanna Wojciechowicz, Jakub Antczak +3
Selecting the top- elements is a fundamental operation for inducing sparsity in large-scale models and optimization problems, enabling robust expert activation, token routing or…
Interpretability-Guided Soft Pruning of Attention Heads in Vision Transformers
Kamil Książek, Piotr Suszyński, Michał Jan Włodarczyk +2
Vision foundation models, such as DINOv2, learn highly expressive representations but rely on massive, opaque architectures that demand substantial computational power and memory.…
SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense
Patryk Krukowski, Łukasz Gorczyca, Piotr Helm +2
Continual learning under adversarial conditions remains an open problem, as existing methods often compromise either robustness, scalability, or both. We propose a novel framework…
FeNeC: Enhancing Continual Learning via Feature Clustering with Neighbor- or Logit-Based Classification
Kamil Książek, Hubert Jastrzębski, Bartosz Trojan +3
The ability of deep learning models to learn continuously is essential for adapting to new data categories and evolving data distributions. In recent years, approaches leveraging f…
HyConEx: Hypernetwork classifier with counterfactual explanations for tabular data
Patryk Marszałek, Kamil Książek, Oleksii Furman +3
In recent years, there has been a growing interest in explainable AI methods. In addition to making accurate predictions, we also want to understand what the model's decision is ba…
SEMU: Singular Value Decomposition for Efficient Machine Unlearning
Marcin Sendera, Łukasz Struski, Kamil Książek +3
While the capabilities of generative foundational models have advanced rapidly in recent years, methods to prevent harmful and unsafe behaviors remain underdeveloped. Among the pre…
HINT: Hypernetwork Approach to Training Weight Interval Regions in Continual Learning
Patryk Krukowski, Anna Bielawska, Kamil Książek +3
Recently, a new Continual Learning (CL) paradigm was presented to control catastrophic forgetting, called Interval Continual Learning (InterContiNet), which relies on enforcing int…
HyperMask: Adaptive Hypernetwork-based Masks for Continual Learning
Kamil Książek, Przemysław Spurek
Artificial neural networks suffer from catastrophic forgetting when they are sequentially trained on multiple tasks. Many continual learning (CL) strategies are trying to overcome…