Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Krzyziński, Mateusz"'
Autor:
Grzyb, Mateusz, Krzyziński, Mateusz, Sobieski, Bartłomiej, Spytek, Mikołaj, Pieliński, Bartosz, Dan, Daniel, Wróblewska, Anna
This project explores the application of Natural Language Processing (NLP) techniques to analyse United Nations General Assembly (UNGA) speeches. Using NLP allows for the efficient processing and analysis of large volumes of textual data, enabling th
Externí odkaz:
http://arxiv.org/abs/2406.13553
Autor:
Langbein, Sophie Hanna, Krzyziński, Mateusz, Spytek, Mikołaj, Baniecki, Hubert, Biecek, Przemysław, Wright, Marvin N.
With the spread and rapid advancement of black box machine learning models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade. This is particularly re
Externí odkaz:
http://arxiv.org/abs/2403.10250
Autor:
Spytek, Mikołaj, Krzyziński, Mateusz, Langbein, Sophie Hanna, Baniecki, Hubert, Wright, Marvin N., Biecek, Przemysław
Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread adoption is hindered by a lack of user-friendly tools to explain their
Externí odkaz:
http://arxiv.org/abs/2308.16113
Autor:
Kobylińska, Katarzyna, Krzyziński, Mateusz, Machowicz, Rafał, Adamek, Mariusz, Biecek, Przemysław
The machine learning modeling process conventionally culminates in selecting a single model that maximizes a selected performance metric. However, this approach leads to abandoning a more profound analysis of slightly inferior models. Particularly in
Externí odkaz:
http://arxiv.org/abs/2308.11446
Autor:
Pfeifer, Bastian, Krzyzinski, Mateusz, Baniecki, Hubert, Saranti, Anna, Holzinger, Andreas, Biecek, Przemyslaw
Explainable AI (XAI) is an increasingly important area of machine learning research, which aims to make black-box models transparent and interpretable. In this paper, we propose a novel approach to XAI that uses the so-called counterfactual paths gen
Externí odkaz:
http://arxiv.org/abs/2307.07764
The usual goal of supervised learning is to find the best model, the one that optimizes a particular performance measure. However, what if the explanation provided by this model is completely different from another model and different again from anot
Externí odkaz:
http://arxiv.org/abs/2302.13356
Autor:
Wilczyński, Piotr, Żółkowski, Artur, Krzyziński, Mateusz, Wiśnios, Emilia, Pieliński, Bartosz, Giziński, Stanisław, Sienkiewicz, Julian, Biecek, Przemysław
This paper introduces HADES, a novel tool for automatic comparative documents with similar structures. HADES is designed to streamline the work of professionals dealing with large volumes of documents, such as policy documents, legal acts, and scient
Externí odkaz:
http://arxiv.org/abs/2302.13099
Autor:
Żółkowski, Artur, Krzyziński, Mateusz, Wilczyński, Piotr, Giziński, Stanisław, Wiśnios, Emilia, Pieliński, Bartosz, Sienkiewicz, Julian, Biecek, Przemysław
The number of standardized policy documents regarding climate policy and their publication frequency is significantly increasing. The documents are long and tedious for manual analysis, especially for policy experts, lawmakers, and citizens who lack
Externí odkaz:
http://arxiv.org/abs/2211.05852
Publikováno v:
Knowledge-Based Systems, vol. 262, 110234, 2023
Machine and deep learning survival models demonstrate similar or even improved time-to-event prediction capabilities compared to classical statistical learning methods yet are too complex to be interpreted by humans. Several model-agnostic explanatio
Externí odkaz:
http://arxiv.org/abs/2208.11080
Autor:
Bartnik, Krzysztof1 (AUTHOR) krzysztof.bartnik@wum.edu.pl, Krzyziński, Mateusz2 (AUTHOR), Bartczak, Tomasz2 (AUTHOR), Korzeniowski, Krzysztof1 (AUTHOR), Lamparski, Krzysztof1 (AUTHOR), Wróblewski, Tadeusz3 (AUTHOR), Grąt, Michał3 (AUTHOR), Hołówko, Wacław3 (AUTHOR), Mech, Katarzyna4 (AUTHOR), Lisowska, Joanna4 (AUTHOR), Januszewicz, Magdalena1 (AUTHOR), Biecek, Przemysław2 (AUTHOR)
Publikováno v:
Scientific Reports. 6/26/2024, Vol. 14 Issue 1, p1-13. 13p.