Posts by Collection

Karolczak, J., Stefanowski, J.
April 2024 Progress in Polish Artificial Intelligence Research 5 : Proceedings of the 5th Polish Conference on Artificial Intelligence (PP-RAI'2024)

[Paper] [GitHub] [BibTeX]

Ligand Identification in CryoEM and X-ray Maps Using Deep Learning

Karolczak, J., Przybyłowska, A., Szewczyk, K., Taisner W., Heumann, J., Stowell M., Nowicki, M., Brzeziński D.
January 2025 Bioinformatics

[Paper] [GitHub] [BibTeX]

This part looks alike this: identifying important parts of explained instances and prototypes

Karolczak, J., Stefanowski, J.
July 2025

[Preprint] [GitHub] [BibTeX]

An Interpretable Prototype Parts-based Neural Network for Medical Tabular Data

Karolczak, J., Stefanowski, J.
October 2025 The 2nd Workshop on Explainable Artificial Intelligence for the Medical Domain within ECAI 2025 the 28th European Conference on Artificial Intelligence

[GitHub] [BibTeX]

Explaining Data Changes with Prototypes: A Measure-Driven Approach

Karolczak, J., Stefanowski, J.
February 2026 Information Fusion

[Paper] [GitHub] [BibTeX]

teaching

Systemy uczące się

Laboratory classes 2025 Poznan University of Technology, Faculty of Computing and Telecommunications

Jacek Karolczak

Posts by Collection

honours

1st place in the Best Hacking League hackathon, category Artificial Intelligence

2nd place in the HackYeah! hackathon, category: “Best place for … everything”

1st place in the CuValley Hack hackathon

Prize for the best prototype in the AKAI Code hackathon

Distinction in the III contest for the best BSc projects and theses in the field of computing science

SemEval 2024 BRAINTEASER: A Novel Task Defying Common Sense

Summa Cum Laude

Distinction in the XLI contest for the best MSc theses in the field of computing science

publications

A-PETE: Adaptive Prototype Explanations of Tree Ensembles

Ligand Identification in CryoEM and X-ray Maps Using Deep Learning

This part looks alike this: identifying important parts of explained instances and prototypes

An Interpretable Prototype Parts-based Neural Network for Medical Tabular Data

Explaining Data Changes with Prototypes: A Measure-Driven Approach

talks

Ligand classification using sparse deep neural networks

Brytmind: A GPU Accelerated Machine Learning Framework for Relational Data Analytics

Measuring ontology concepts’ similarity

Unlocking the potential of large language models: Enhancing chatbots with a GPU-centric vector database

Duże problemy dużych modeli językowych i ich małe rozwiązanie

Building AI Applications with GPU Vector Databases

A-PETE: Adaptive Prototype Explanations of Tree Ensembles

Explainable AI: Moving from Numbers to Meaningful Insights

This part looks alike this: identifying important parts of explained instances and prototypes

An Interpretable Prototype Parts-based Neural Network for Medical Tabular Data

teaching

Systemy uczące się