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 MarszaÅek, Tomasz KuÅmierczyk, Witold WydmaÅski +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 WydmaÅski, 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…
RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets
Piotr GaiÅski, MichaÅ 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…
StyleAutoEncoder for manipulating image attributes using pre-trained StyleGAN
Andrzej Bedychaj, Jacek Tabor, Marek Åmieja
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…
Augmentation-aware Self-supervised Learning with Conditioned Projector
Marcin PrzewiÄźlikowski, Mateusz Pyla, Bartosz ZieliÅski +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…