16 citations · 21 across the 21 of their papers we have counts for
8 papers · 1 filter
Prof-K: Probabilistic One-Pass Filtering for Efficient Top-k Selection
Tadeusz Dziarmaga, Witold Sikora, Łukasz Struski +2
Top-k selection is a fundamental computational primitive with applications spanning databases, information retrieval, signal processing, and modern machine learning workloads, incl…
Stop Marginalizing My Dreams: Model Inversion via Laplace Kernel for Continual Learning
Patryk Krukowski, Jacek Tabor, Przemysław Spurek +2
Data-free continual learning (DFCIL) relies on model inversion to synthesize pseudo-samples and mitigate catastrophic forgetting. However, existing inversion methods are fundamenta…
SoftSAE: Dynamic Top-K Selection for Adaptive Sparse Autoencoders
Jakub Stępień, Marcin Mazur, Jacek Tabor +1
Sparse Autoencoders (SAEs) have become an important tool in mechanistic interpretability, helping to analyze internal representations in both Large Language Models (LLMs) and Visio…
InTAct: Interval-based Task Activation Consolidation for Continual Learning
Patryk Krukowski, Jan Miksa, Piotr Helm +3
Continual learning is a fundamental challenge in artificial intelligence that requires networks to acquire new knowledge while preserving previously learned representations. Despit…
FreSh: Frequency Shifting for Accelerated Neural Representation Learning
Adam Kania, Marko Mihajlovic, Sergey Prokudin +2
Implicit Neural Representations (INRs) have recently gained attention as a powerful approach for continuously representing signals such as images, videos, and 3D shapes using multi…
Make Interval Bound Propagation great again
Patryk Krukowski, Daniel Wilczak, Jacek Tabor +2
In various scenarios motivated by real life, such as medical data analysis, autonomous driving, and adversarial training, we are interested in robust deep networks. A network is ro…
ProPML: Probability Partial Multi-label Learning
Łukasz Struski, Adam Pardyl, Jacek Tabor +1
Partial Multi-label Learning (PML) is a type of weakly supervised learning where each training instance corresponds to a set of candidate labels, among which only some are true. In…
Cramer-Wold AutoEncoder
Szymon Knop, Jacek Tabor, Przemysław Spurek +3
We propose a new generative model, Cramer-Wold Autoencoder (CWAE). Following WAE, we directly encourage normality of the latent space. Our paper uses also the recent idea from Slic…