Zobrazeno 1 - 10
of 197
pro vyhledávání: '"VAN KEULEN, MAURICE"'
Autor:
Wu, Boqian, Xiao, Qiao, Wang, Shunxin, Strisciuglio, Nicola, Pechenizkiy, Mykola, van Keulen, Maurice, Mocanu, Decebal Constantin, Mocanu, Elena
It is generally perceived that Dynamic Sparse Training opens the door to a new era of scalability and efficiency for artificial neural networks at, perhaps, some costs in accuracy performance for the classification task. At the same time, Dense Train
Externí odkaz:
http://arxiv.org/abs/2410.03030
Autor:
Junior, Sylvio Barbon, Ceravolo, Paolo, Groppe, Sven, Jarrar, Mustafa, Maghool, Samira, Sèdes, Florence, Sahri, Soror, Van Keulen, Maurice
A Language Model is a term that encompasses various types of models designed to understand and generate human communication. Large Language Models (LLMs) have gained significant attention due to their ability to process text with human-like fluency a
Externí odkaz:
http://arxiv.org/abs/2406.06596
Autor:
Pathak, Shreyasi, Schlötterer, Jörg, Veltman, Jeroen, Geerdink, Jeroen, van Keulen, Maurice, Seifert, Christin
Deep learning models have achieved high performance in medical applications, however, their adoption in clinical practice is hindered due to their black-box nature. Self-explainable models, like prototype-based models, can be especially beneficial as
Externí odkaz:
http://arxiv.org/abs/2403.20260
Autor:
Wu, Boqian, Xiao, Qiao, Liu, Shiwei, Yin, Lu, Pechenizkiy, Mykola, Mocanu, Decebal Constantin, Van Keulen, Maurice, Mocanu, Elena
Deep neural networks have evolved as the leading approach in 3D medical image segmentation due to their outstanding performance. However, the ever-increasing model size and computation cost of deep neural networks have become the primary barrier to d
Externí odkaz:
http://arxiv.org/abs/2312.04727
Autor:
Pathak, Shreyasi, Schlötterer, Jörg, Geerdink, Jeroen, Veltman, Jeroen, van Keulen, Maurice, Strisciuglio, Nicola, Seifert, Christin
Breast cancer prediction models for mammography assume that annotations are available for individual images or regions of interest (ROIs), and that there is a fixed number of images per patient. These assumptions do not hold in real hospital settings
Externí odkaz:
http://arxiv.org/abs/2310.12677
Autor:
Nauta, Meike, Hegeman, Johannes H., Geerdink, Jeroen, Schlötterer, Jörg, van Keulen, Maurice, Seifert, Christin
Part-prototype models are explainable-by-design image classifiers, and a promising alternative to black box AI. This paper explores the applicability and potential of interpretable machine learning, in particular PIP-Net, for automated diagnosis supp
Externí odkaz:
http://arxiv.org/abs/2307.10404
Autor:
Nauta, Meike, Trienes, Jan, Pathak, Shreyasi, Nguyen, Elisa, Peters, Michelle, Schmitt, Yasmin, Schlötterer, Jörg, van Keulen, Maurice, Seifert, Christin
The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes raised the question of how to evaluate explanations of machine learning (ML) models. While interpretability and explainability are often pres
Externí odkaz:
http://arxiv.org/abs/2201.08164
Autor:
Provoost, Jesper, Wismans, Luc, Van der Drift, Sander, Kamilaris, Andreas, Van Keulen, Maurice
Public road authorities and private mobility service providers need information derived from the current and predicted traffic states to act upon the daily urban system and its spatial and temporal dynamics. In this research, a real-time parking area
Externí odkaz:
http://arxiv.org/abs/1911.13178
Autor:
Provoost, Jesper C., Kamilaris, Andreas, Wismans, Luc J.J., van der Drift, Sander J., van Keulen, Maurice
Publikováno v:
In Internet of Things December 2020 12
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