Zobrazeno 1 - 10
of 288
pro vyhledávání: '"Štiglic Gregor"'
This review examines the development of abstractive NLP-based text summarization approaches and compares them to existing techniques for extractive summarization. A brief history of text summarization from the 1950s to the introduction of pre-trained
Externí odkaz:
http://arxiv.org/abs/2411.11635
Autor:
Kopitar, Leon, Plohl, Nejc, Verboten, Mojca Tancer, Štiglic, Gregor, Watson, Roger, Korošak, Dean
The current system of scholarly publishing is often criticized for being slow, expensive, and not transparent. The rise of open access publishing as part of open science tenets, promoting transparency and collaboration, together with calls for resear
Externí odkaz:
http://arxiv.org/abs/2411.06282
This study explores the effectiveness of Large Language Models in meal planning, focusing on their ability to identify and decompose compound ingredients. We evaluated three models-GPT-4o, Llama-3 (70b), and Mixtral (8x7b)-to assess their proficiency
Externí odkaz:
http://arxiv.org/abs/2411.05892
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
Autor:
Stiglic, Gregor, Kopitar, Leon, Gosak, Lucija, Kocbek, Primoz, He, Zhe, Chakraborty, Prithwish, Meyer, Pablo, Bian, Jiang
Primary care professionals struggle to keep up to date with the latest scientific literature critical in guiding evidence-based practice related to their daily work. To help solve the above-mentioned problem, we employed generative artificial intelli
Externí odkaz:
http://arxiv.org/abs/2307.15715
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
Publikováno v:
In Preventive Medicine Reports January 2024 37
Machine Learning (ML) models are often complex and difficult to interpret due to their 'black-box' characteristics. Interpretability of a ML model is usually defined as the degree to which a human can understand the cause of decisions reached by a ML
Externí odkaz:
http://arxiv.org/abs/2006.13815
Autor:
Stiglic, Gregor, Kocbek, Primoz, Fijacko, Nino, Zitnik, Marinka, Verbert, Katrien, Cilar, Leona
Publikováno v:
WIREs Data Mining Knowl Discov (2020)
There is a need of ensuring machine learning models that are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end-users. Further, interpretable machine learning models allow heal
Externí odkaz:
http://arxiv.org/abs/2002.08596