7 citations · 8 across the 5 of their papers we have counts for
6 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…
ZEUS: Zero-shot Embeddings for Unsupervised Separation of Tabular Data
Patryk Marszałek, Tomasz Kuśmierczyk, Witold Wydmański +2
Clustering tabular data remains a significant open challenge in data analysis and machine learning. Unlike for image data, similarity between tabular records often varies across da…
VisTabNet: Adapting Vision Transformers for Tabular Data
Witold Wydmański, Ulvi Movsum-zada, Jacek Tabor +1
Although deep learning models have had great success in natural language processing and computer vision, we do not observe comparable improvements in the case of tabular data, whic…
StyleAutoEncoder for manipulating image attributes using pre-trained StyleGAN
Andrzej Bedychaj, Jacek Tabor, Marek Śmieja
Deep conditional generative models are excellent tools for creating high-quality images and editing their attributes. However, training modern generative models from scratch is ver…
RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets
Piotr Gaiński, Michał Koziarski, Krzysztof Maziarz +3
Single-step retrosynthesis aims to predict a set of reactions that lead to the creation of a target molecule, which is a crucial task in molecular discovery. Although a target mole…
Augmentation-aware Self-supervised Learning with Conditioned Projector
Marcin Przewięźlikowski, Mateusz Pyla, Bartosz Zieliński +3
Self-supervised learning (SSL) is a powerful technique for learning from unlabeled data. By learning to remain invariant to applied data augmentations, methods such as SimCLR and M…