5 citations · 6 across the 5 of their papers we have counts for
5 papers
Schematize: An Agentic System for Generating and Refining Information-Extraction Schemas for Legal Research
Albert Sawczyn, Jakub Binkowski, Kamil Tagowski +3
Empirical legal research often relies on turning research questions into structured data extracted from large collections of rulings and judgments. Designing the extraction schema…
RAFEN -- Regularized Alignment Framework for Embeddings of Nodes
Kamil Tagowski, Piotr Bielak, Jakub Binkowski +1
Learning representations of nodes has been a crucial area of the graph machine learning research area. A well-defined node embedding model should reflect both node features and the…
This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish
Łukasz Augustyniak, Kamil Tagowski, Albert Sawczyn +9
The availability of compute and data to train larger and larger language models increases the demand for robust methods of benchmarking the true progress of LM training. Recent yea…
On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction
Edward Elson Kosasih, Joaquin Cabezas, Xavier Sumba +5
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. The PCQM4M-LSC dataset defines a…
FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings
Piotr Bielak, Kamil Tagowski, Maciej Falkiewicz +2
Representation learning on graphs has emerged as a powerful mechanism to automate feature vector generation for downstream machine learning tasks. The advances in representation on…