10 papers
In-Context Density Estimation for Tabular Data
Patryk Marszałek, Jacek Tabor, Marek Śmieja
Density estimation underlies many unsupervised tasks on tabular data such as anomaly detection, out-of-distribution detection, and data augmentation. Although all these problems re…
Conceptualizing Embeddings: Sparse Disentanglement for Vision-Language Models
Piotr Kubaty, Patryk MarszaÅek, Åukasz Struski +3
Vision-language models learn powerful multimodal embeddings, yet their internal semantics remain opaque. While sparse autoencoders (SAEs) can extract interpretable features, they r…
Bayesian Fine-tuning in Projected Subspaces
Viktar Dubovik, Patryk MarszaÅek, Jacek Tabor +1
Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of large models by decomposing weight updates into low-rank matrices, significantly reducing storage and computat…
TACTIC for Navigating the Unknown: Tabular Anomaly deteCTion via In-Context inference
Patryk MarszaÅek, Tomasz KuÅmierczyk, Marek Åmieja
Anomaly detection for tabular data has been a long-standing unsupervised learning problem that remains a major challenge for current deep learning models. Recently, in-context lear…
HyConEx: Hypernetwork classifier with counterfactual explanations for tabular data
Patryk MarszaÅek, Kamil KsiÄ Å¼ek, 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…
CounterFlowNet: From Minimal Changes to Meaningful Counterfactual Explanations
Oleksii Furman, Patryk MarszaÅek, Jan MasÅowski +3
Counterfactual explanations (CFs) provide human-interpretable insights into model's predictions by identifying minimal changes to input features that would alter the model's output…