74 citations · 272 across the 40 of their papers we have counts for
5 papers · 1 filter
ChiENN: Embracing Molecular Chirality with Graph Neural Networks
Piotr Gaiński, Michał Koziarski, Jacek Tabor +1
Graph Neural Networks (GNNs) play a fundamental role in many deep learning problems, in particular in cheminformatics. However, typical GNNs cannot capture the concept of chirality…
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…
r-softmax: Generalized Softmax with Controllable Sparsity Rate
Klaudia Bałazy, Łukasz Struski, Marek Śmieja +1
Nowadays artificial neural network models achieve remarkable results in many disciplines. Functions mapping the representation provided by the model to the probability distribution…
HyperTab: Hypernetwork Approach for Deep Learning on Small Tabular Datasets
Witold Wydmański, Oleksii Bulenok, Marek Śmieja
Deep learning has achieved impressive performance in many domains, such as computer vision and natural language processing, but its advantage over classical shallow methods on tabu…
Contrastive Hierarchical Clustering
Michał Znaleźniak, Przemysław Rola, Patryk Kaszuba +2
Deep clustering has been dominated by flat models, which split a dataset into a predefined number of groups. Although recent methods achieve an extremely high similarity with the g…