3 citations · 13 across the 11 of their papers we have counts for
11 papers
Learning to Perceive "Where": Spatial Pretext Tasks for Robust Self-Supervised Learning
Yang Shen, Yusen Cai, Weronika Hryniewska-Guzik +2
Existing self-supervised learning (SSL) methods primarily learn object-invariant representations but often neglect the spatial structure and relationships among object parts. To ad…
X-ray transferable polyrepresentation learning
Weronika Hryniewska-Guzik, Przemyslaw Biecek
The success of machine learning algorithms is inherently related to the extraction of meaningful features, as they play a pivotal role in the performance of these algorithms. Centr…
Cartesian Genetic Programming Approach for Designing Convolutional Neural Networks
Maciej Krzywda, Szymon Łukasik, Amir Gandomi H
The present study covers an approach to neural architecture search (NAS) using Cartesian genetic programming (CGP) for the design and optimization of Convolutional Neural Networks…
Task scheduling for autonomous vehicles in the Martian environment
Wojciech Burzyński, Mariusz Kaleta
In the paper, we introduced a novel variant of Electric VRP/TSP, the Solar Powered Rover Routing Problem (SPRRP), to tackle the routing of energy-constrained autonomous electric ve…
Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema
Waldemar Kołodziejczyk, Mariusz Kaleta
This paper explores the application of Reinforcement Learning (RL) to the two-dimensional rectangular packing problem. We propose a reduced representation of the state and action s…
Rico: extended TIAGo robot towards up-to-date social and assistive robot usage scenarios
Tomasz Winiarski, Wojciech Dudek, Daniel Giełdowski
Social and assistive robotics have vastly increased in popularity in recent years. Due to the wide range of usage, robots executing such tasks must be highly reliable and possess e…
CNN-based explanation ensembling for dataset, representation and explanations evaluation
Weronika Hryniewska-Guzik, Luca Longo, Przemysław Biecek
Explainable Artificial Intelligence has gained significant attention due to the widespread use of complex deep learning models in high-stake domains such as medicine, finance, and…
A comparative analysis of deep learning models for lung segmentation on X-ray images
Weronika Hryniewska-Guzik, Jakub Bilski, Bartosz Chrostowski +2
Robust and highly accurate lung segmentation in X-rays is crucial in medical imaging. This study evaluates deep learning solutions for this task, ranking existing methods and analy…
NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniques
Weronika Hryniewska-Guzik, Bartosz Sawicki, Przemysław Biecek
This paper presents a comprehensive comparative analysis of explainable artificial intelligence (XAI) ensembling methods. Our research brings three significant contributions. First…
Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans
Weronika Hryniewska-Guzik, Maria Kędzierska, Przemysław Biecek
Lung cancer and covid-19 have one of the highest morbidity and mortality rates in the world. For physicians, the identification of lesions is difficult in the early stages of the d…
Challenges facing the explainability of age prediction models: case study for two modalities
Mikolaj Spytek, Weronika Hryniewska-Guzik, Jaroslaw Zygierewicz +2
The prediction of age is a challenging task with various practical applications in high-impact fields like the healthcare domain or criminology. Despite the growing number of model…