RAFEN -- Regularized Alignment Framework for Embeddings of Nodes

1 code implementation • 3 Mar 2023 • Kamil Tagowski, Piotr Bielak, Jakub Binkowski, Tomasz Kajdanowicz

A well-defined node embedding model should reflect both node features and the graph structure in the final embedding.

This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish

1 code implementation • 23 Nov 2022 • Łukasz Augustyniak, Kamil Tagowski, Albert Sawczyn, Denis Janiak, Roman Bartusiak, Adrian Szymczak, Marcin Wątroba, Arkadiusz Janz, Piotr Szymański, Mikołaj Morzy, Tomasz Kajdanowicz, Maciej Piasecki

In this paper, we introduce LEPISZCZE (the Polish word for glew, the Middle English predecessor of glue), a new, comprehensive benchmark for Polish NLP with a large variety of tasks and high-quality operationalization of the benchmark.

Benchmarking

On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction

1 code implementation • 29 Jun 2021 • Edward Elson Kosasih, Joaquin Cabezas, Xavier Sumba, Piotr Bielak, Kamil Tagowski, Kelvin Idanwekhai, Benedict Aaron Tjandra, Arian Rokkum Jamasb

In order to advance large-scale graph machine learning, the Open Graph Benchmark Large Scale Challenge (OGB-LSC) was proposed at the KDD Cup 2021.

Active Learning Molecular Property Prediction +1

FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings

1 code implementation • 6 Apr 2019 • Piotr Bielak, Kamil Tagowski, Maciej Falkiewicz, Tomasz Kajdanowicz, Nitesh V. Chawla

Experimental results on several downstream tasks, over seven real-world data sets, show that FILDNE is able to reduce memory and computational time costs while providing competitive quality measure gains with respect to the contemporary methods for representation learning on dynamic graphs.

Dynamic graph embedding Incremental Learning +2