Zobrazeno 1 - 10
of 6 633
pro vyhledávání: '"Verbert, A."'
Autor:
Serras, Felipe R., Finger, Marcelo
Publikováno v:
SERRAS, F. R.; FINGER, M. verBERT: Automating Brazilian Case Law Document Multi-label Categorization Using BERT. In 13th Brazilian Simposiun on Human Language and Information Technology (STIL), 2021. pp. 237-246
In this work, we carried out a study about the use of attention-based algorithms to automate the categorization of Brazilian case law documents. We used data from the Kollemata Project to produce two distinct datasets with adequate class systems. The
Externí odkaz:
http://arxiv.org/abs/2203.06224
Cardiovascular diseases (CVDs), the leading cause of death worldwide, can be prevented in most cases through behavioral interventions. Therefore, effective communication of CVD risk and projected risk reduction by risk factor modification plays a cru
Externí odkaz:
http://arxiv.org/abs/2406.18690
Publikováno v:
Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP Adjunct '24), July 1--4, 2024, Cagliari, Italy
Biases in Artificial Intelligence (AI) or Machine Learning (ML) systems due to skewed datasets problematise the application of prediction models in practice. Representation bias is a prevalent form of bias found in the majority of datasets. This bias
Externí odkaz:
http://arxiv.org/abs/2407.09485
Publikováno v:
Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP Adjunct '24), July 1--4, 2024, Cagliari, Italy
With the increasing adoption of Artificial Intelligence (AI) systems in high-stake domains, such as healthcare, effective collaboration between domain experts and AI is imperative. To facilitate effective collaboration between domain experts and AI s
Externí odkaz:
http://arxiv.org/abs/2405.13038
Publikováno v:
Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), May 11--16, 2024, Honolulu, HI, USA
Explanations in interactive machine-learning systems facilitate debugging and improving prediction models. However, the effectiveness of various global model-centric and data-centric explanations in aiding domain experts to detect and resolve potenti
Externí odkaz:
http://arxiv.org/abs/2402.00491
In the realm of interactive machine-learning systems, the provision of explanations serves as a vital aid in the processes of debugging and enhancing prediction models. However, the extent to which various global model-centric and data-centric explan
Externí odkaz:
http://arxiv.org/abs/2310.02063
While research on explainable AI (XAI) is booming and explanation techniques have proven promising in many application domains, standardised human-centred evaluation procedures are still missing. In addition, current evaluation procedures do not asse
Externí odkaz:
http://arxiv.org/abs/2308.06274
Publikováno v:
IUI '23: 28th International Conference on Intelligent User Interfaces Proceedings (2023)
Researchers have widely acknowledged the potential of control mechanisms with which end-users of recommender systems can better tailor recommendations. However, few e-learning environments so far incorporate such mechanisms, for example for steering
Externí odkaz:
http://arxiv.org/abs/2303.00098
Explainable artificial intelligence is increasingly used in machine learning (ML) based decision-making systems in healthcare. However, little research has compared the utility of different explanation methods in guiding healthcare experts for patien
Externí odkaz:
http://arxiv.org/abs/2302.10671
Autor:
Adrijana Svenšek, Mateja Lorber, Lucija Gosak, Katrien Verbert, Zalika Klemenc-Ketis, Gregor Stiglic
Publikováno v:
JMIR Public Health and Surveillance, Vol 10, p e60128 (2024)
BackgroundSupporting and understanding the health of patients with chronic diseases and cardiovascular disease (CVD) risk is often a major challenge. Health data are often used in providing feedback to patients, and visualization plays an important r
Externí odkaz:
https://doaj.org/article/bb49dcd2173342e7844864a667a7a31b
